{
"cells": [
{
"cell_type": "markdown",
"id": "a6c2b8c8",
"metadata": {},
"source": [
"# Background Subtraction of YMnO3\n",
"\n",
"In a dataset, not only the desired signal but also a lot of background points are often measured, which may initially seem wasteful. However, if we assume that the background from the sample environment, sample holder, and other general background sources is independent of the sample rotation ($A_3$), then removing the regions of reciprocal space with a signal (hereafter denoted as foreground) will enable us to use all the remaining points to approximate the background. However, certain requirements need to be fulfilled. By integrating along A3, we end up with a background intensity that depends on $|Q|$ and energy transfer. Sampling from this intensity gives us a powder-averaged background estimator. This method only works if the following points are true:\n",
"\n",
"- The foreground intensity has a limited extend\n",
"- Enough reciprocal space has been measured\n",
" - All $|Q|$ and energy points must have some coverage\n",
"- Masks must be possible to create to remove the foreground\n",
"\n",
"In the following example, we will use the data from YMnO3 to demonstrate how the background can be defined and help to subtract it from the signal.\n",
"\n",
"**Warning**: If we do not remove all the foreground intensity and include it in the background calculation, we will end up over-subtracting the background esulting in negative scattering intensity. Negative intensities are a sign of oversubtraction, but they may also to some extend occur naturally due to counting uncertainties. The error bars for 1D cuts are propagated by estimating the root of errors added in quadrature. \n"
]
},
{
"cell_type": "markdown",
"id": "b0e62cd0",
"metadata": {},
"source": [
"## Example using YMnO3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "60adcf27",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:10.547894Z",
"iopub.status.busy": "2023-07-25T11:36:10.547426Z",
"iopub.status.idle": "2023-07-25T11:36:14.183571Z",
"shell.execute_reply": "2023-07-25T11:36:14.182479Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from MJOLNIR.Data import DataSet,Mask\n",
"from MJOLNIR import _tools # Usefull tools useful across MJOLNIR\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['figure.figsize'] = [14, 12]\n",
"plt.rcParams['figure.dpi'] = 200\n",
"\n",
"numbers = '136-137' # String of data numbers\n",
"fileList = _tools.fileListGenerator(numbers,r'C:\\Users\\lass_j\\Documents\\CAMEA2018',2018) # Create file list from 2018 in specified folder\n",
"\n",
"ds = DataSet.DataSet(fileList)\n",
"ds.convertDataFile();\n"
]
},
{
"cell_type": "markdown",
"id": "b5ba3835",
"metadata": {},
"source": [
"To make a background subtraction, first we need to remove the actual signal in the data. This is done by generating suitable masks; notice that the exact centre of the dispersion is a little off."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3671af3a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:14.186571Z",
"iopub.status.busy": "2023-07-25T11:36:14.186571Z",
"iopub.status.idle": "2023-07-25T11:36:14.198802Z",
"shell.execute_reply": "2023-07-25T11:36:14.197913Z"
}
},
"outputs": [],
"source": [
"# Center is a little off...\n",
"center = np.array([1.011,0.073])\n",
"centerHKL = np.array([*center,0.0])\n",
"radiusPoint = np.array([1.112,0.057])\n",
"\n",
"# Calculate to Qx Qy instead of HKL as data is from YMnO3, i.e. a hexagonal lattice\n",
"centerQxQy = ds[0].sample.calculateHKLToQxQy(*center,0)\n",
"radiusPointQxQy = ds[0].sample.calculateHKLToQxQy(*radiusPoint,0)\n",
"BGMask = Mask.circleMask(centerQxQy,radiusPointQxQy,coordinates=['qx','qy'])"
]
},
{
"cell_type": "markdown",
"id": "4730766c",
"metadata": {},
"source": [
"First the centre is found from the a View3D object (not shown), and then calcualted into ($Q_x$,$Q_y$) through the conversion on the samplde object found in any of the DataFiles. This is done in order to make a circle in ($Q_x$,$Q_y$) space and not in ($H$,$K$) space as the latter is skewed due to the sample having a hexagonal symmetry in the current ($H$,$K$,$0$) scattering plane. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "13c206ab",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:14.203039Z",
"iopub.status.busy": "2023-07-25T11:36:14.202930Z",
"iopub.status.idle": "2023-07-25T11:36:14.563443Z",
"shell.execute_reply": "2023-07-25T11:36:14.562549Z"
}
},
"outputs": [],
"source": [
"# Generate background estimation from powder calculation\n",
"# by only using data outside the background mask combined with the foreground mask.\n",
"# Either provide the mask as a masking object or as a list of boolean arrays of size [df.I.shape for df in ds]\n",
"\n",
"ds.generateBackgroundModel(backgroundMask = BGMask, dQ = 0.025, dE = 0.05,\n",
" combineForegroundMask = True)"
]
},
{
"cell_type": "markdown",
"id": "3b7959be",
"metadata": {},
"source": [
"What this does is that it applies the mask to the data and performs a powder average, i.e. calculates $|Q|$ for all data points and performes a binning of the intensities in the provided bin sizes. For the best result one should use bin sizes large enough to contain multiple pixels from the instrument but small enough as to not smear out background features. Thus, choosing the correct binning is quite difficult and some itteration is needed."
]
},
{
"cell_type": "markdown",
"id": "00151fc2",
"metadata": {},
"source": [
"Notice the additional flag of combining with the foreground mask. If the DataFiles have any masking, either due to spurios features (Currat-Axe) or non-functioning detector tubes, we would want this parts of the data to also be masked out in the background. If this is not performed, these unwanted features might skew up the powder-averaged background and thus render the background unuseful.\n",
"\n",
"By running the above code a BackgroundModel object is created and attached to the DataSet. The found intensities are plotted by"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "20edf0b7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:14.567155Z",
"iopub.status.busy": "2023-07-25T11:36:14.566992Z",
"iopub.status.idle": "2023-07-25T11:36:15.036826Z",
"shell.execute_reply": "2023-07-25T11:36:15.035979Z"
}
},
"outputs": [
{
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CrLLQCLPOWD1ujHsYtcfjcsfOHYvrlLTrtngf3eJ3S640W2G2dNp4mI08GB+b9lgtzIaSP0O1JofjUNLKVFxvffTUMGtui8tZKz4fQ8vtMKs04rZWmnG5Tm0qqXMlzCSpshTnneF4HytDyY/MpFx7cjSuc7kZZl6thpnV8x/fnZ2T8TaT4+O1eJvZdVVZivt/ZySus5L0DVuJM0myTicOk8wacVt9OD6u1o77eGc87sfZfmT3FEmyDd7nlNRbnVkMs6Uzt4XZ6D1zYebnPC5uC9BDDGQAAACAEnCZ2p4PkMuEpWUAAAAACocZGQAAAKAkOgM0TzE4ewoAAACgNBjIAAAAACgclpYBAAAAJeAutX1w5ikGZ08BAAAAlAYzMgAAAEApmDri5ZcBAAAAoG8xkAEAAABQOCwtAwAAAErAxcP+AAAAANDXmJEBAAAASqI9QPMUg7OnAAAAAEqDGRlgwFRml8JsdL6RF15phdHYUnND5VSJXyYy/UtLUmdlaSXMdi23s1rVGY1vi9XZ5PhU4/2ozcbtqc4uh1llIdleJT46lZbH5SSN3TUXZp2ReP9rs3GdnXo1Lnd4IS43Phxm1kzOcdwUVRaTvihJnfj4VOYW43K15EfmUnIek3OloTgzj9tpi/m1Ws3yrD2tTrzNkVq8vbn4vlKdSfpjO78eU8kxVz1uq0+MxuWSY5M9dlCZjvu4WnE/tno9LidJzbgvD00nx3Us2cf5uI+P3RHfq3x8JMwqR5KbA9BDDGQAAACAEnCZOs77yAAAAABA32IgAwAAAKBwWFoGAAAAlASvWgYAAAAAfYwZGQAAAKAEXFIne4m9khmcPQUAAABQGgxkAAAAABQOS8sAAACAUjC1xfvIAAAAAEDfYkYGAAAAKAEe9gcAAACAPsdABgAAAEDhsLQMAAAAKIlBetifgQwwaFqtOFtJMkkaqsbZzGKcVZNynU6cNZJbVDWZUF5ajovFpVbz++bjcGQ4ztrtMKo1x+Jyi0thZB2PyyX7X/WknCSbT47PoUacTYxvqD1u8Q/V6r1H4jrHR8PImklfXYiP6WqDNnZcU634/NtCfG349sm43EzSF2u1vD1zcVlvNONt1uN6q/c/GG9vuB5n2fFuJ9d/ZZ1fxpJ+pXgXZYfjPp7eq7J7Z1YuuTdoOWmL8nOV1WvJvdwbyTbnF+I6F0fiOpN7LtBLDGQAAACAEnA3HvYHAAAAgH7GQAYAAABA4bC0DAAAACiJNkvLAAAAAKB/MSMDAAAAlIBL6gzQyy8zIwMAAACgcBjIAAAAACgclpYBAAAApWB9/bC/mVUk/Z6kp0tqSPopd79zTf7zki6T1JH0q+7+kay+ngxkzKwq6UpJ52p1ud5r3f22Nfm/kfR6SS1Jt0r6WXfvmNnnJc12v+1r7v6qXrQPAAAAwKZ7maQRd3+umV0g6V2Svl+SzGy7pNdJepKkcUlfkLT5AxlJl0qSuz/PzC6S9I41jRyV9HZJT3X3RTP7E0kvNbNPSDJ3v6hHbQIAAABKyyV1vK8f9r9Q0nWS5O6fM7N9a7IFSd/Q6iBmXKuzMqmezD25+59Lurz76eMkTa+JG5K+w90Xu58PSVrW6hTTmJl9wsw+1R2lfQszu9zMDpjZgUOHDvWi+QAAAAA2ZvdDv6t3Py5fk01JmlnzedvM1k6s3C3pnyR9XtJvr7ehnj0j4+4tM3uvpB+Q9MNrvt6R9IAkmdm/lzQh6S8lfZuk35D0B5LOlnStmZ3r7q3j6t0vab8k7du3z3vVfgAAAACP2GF33xdks5Im13xeWfO7/iWSHiPp8d3PP25mN7n7zdGGevqwv7v/uJn9gqS/NbMnu/uC9M0HfX5d0jmSfsjd3czukHSnu7ukO8zsSHdn7u5lGwEAAICyaPf3ixLfpNVHUD7QXX1165rsmKQlSY3u2GBa0vassl497P8KSY9193dKWtTqGre169x+X6tLzF7WnaGRpFdLeqqknzWz07Q69XR/L9oHDLR6Pc5WWnEmSUPJLaOalK1W46zdjrNaLc5ayfZqSTubK3G2XtksS9hSMwmTtcyd5NjU42PTGUvOsaTq9Hwcjo6kZUOtuK2W7aPHE+ud8eEwq0wvbKhOSXnfqSbHLiuXaSTnv5MsAW8nWWs532ZyDCzrx1m5pM9l5TzZ/7TO9WT3smZyzLPjmp2P5P7nS+ucj4CNjeZ5du+cGI+zRiOuczi+rlTZ2C/A2X5ccvYVadlr//nXN7RNFNZHJL3QzD4jySS9yszeqNXJjI+a2Qskfc7MOpJu1OqqrVCvZmT+TNIfmtnfSKpp9RXKfsDMJiQdkPSTkm6Q9KnuD7jfkvQ/Jf2Rmd2o1WeVXn38sjIAAAAAxdSdwHjtcV/+8pr8bZLedqL19WQg011C9qPJt0RD/st60BwAAACg9FzW769adlL19SI6AAAAAHg4PX3YHwAAAMDm6QzQPMXg7CkAAACA0mAgAwAAAKBwWFoGAAAAlIC71OZhfwAAAADoX8zIAAAAACXByy8DAAAAQB9jIAMAAACgcFhaBgAAAJSAy9TxwZmnGJw9BQAAAFAazMgAAAAAJdHW4Dzsz0AGGDCL5+wKs5F7htOy5h5mjSfsDrP60aW4zpV2mK3sGguzoblmmLUm6mG23v29shy3Z/mU+PgMH43b09wet6c23woza8fHO8sqC3FbJKlz6o4w82p8gLJtqtWJyzXjffSkLY098fkfTvqiVyfDTJKU7WOyH9k+dsZq8ebmG2HW2DsRZkMTI2GWtlOSteN+nF1zWZbxSrzAw5orccFa8mtIco4lSZ0kn4j7jpL2+Gh8rS4+fnuYjRyM73HVY/Nh1jhrZ5hJUu3Ycpit7Ij7Ry2557am4nKVZnz+q7NxnT4SH7dKsv/Ao8XSMgAAAACFw4wMAAAAUAIu3kcGAAAAAPoaMzIAAABAKfDyywAAAADQ1xjIAAAAACgclpYBAAAAJdEZoPeRYUYGAAAAQOEwIwMAAACUgLvU5uWXAQAAAKB/MZABAAAAUDgsLQMAAABKgveRAQAAAIA+xkAGAAAAQOGwtAwooRc961fCbGyhERdcaaX1er0WZiPfOBYXXFqOs3Y7jOorcebV+FVZ6jOLYdZ87Pa4LZKq83Fbx7Njl/Ch+G9G9ftn43K1aph1Ruph1to2nLZnaGElzCqLzXibY/E22xMjYVY/mOyjxedx+MGFMFNSrno43p4k+eRoEnq8yeXkuE3PxXXW4uumNhP3Ka/G/Sbrp6tlk1ct6iRR0q8qR2bCzEbj859qxde4ZvLzqG1TG9tmMz6PVo2vubGvTcflluLrRpX4PA5/7UhcTpKPxudj+GvJ9ZHcq2vN+D5vc/G908fHwqyS3HOztuDkc5k6vGoZAAAAAPQvZmQAAACAkuiIGRkAAAAA6FsMZAAAAAAUDkvLAAAAgBJwiYf9AQAAAKCfMSMDAAAAlETHB2eeYnD2FAAAAEBpMJABAAAAUDgsLQMAAADKwI2H/QEAAACgnzEjAwAAAJSAS+qIGRkAAAAA6FvMyAAlVDk0HWY+vxAX7Pg6FSd/5RlKbice1+vNlbjc0nIY2ehIXK7TCaP615LtSVKrHWcjw3nZaJuzyTFPtmfNZphVLT4X1eR4S5KyY5e0p7o8uqH2KNmPysJSXG483p6mZ+Ms64uS7HAjDluttGyoVouz+cUwSs/VctLOpI9LkhpxWUuOj2XnsVqNs6PTcTZcj7N23N88239Jah+Ls+ReZsl++PRMXC695yTnsbrxvxlbcr/2pK9a0h9tYjyuM9meZedj5/Y4S+7jwKPFQAYAAAAoCR72BwAAAIA+xowMAAAAUAIuZmQAAAAAoK8xkAEAAABQOCwtAwAAAEqCpWUAAAAA0MeYkQEAAABKwGXMyAAAAABAP2MgAwAAAKBwWFoGAAAAlERHLC0DAAAAgL7FQAYAAABA4bC0DAAAACgDH6z3kWEgA5RRqx1GVqvF5YbWuSU0m2HkzZX1WvXw5VbicmlbM8P1OFtaXqdBvrFsZHhj5VqtOOsk5arJD6rRkTiT0v6RtnW5EWdZ38mOeT0+V16JFw1Yp5O0pRpnknx6Ia53bDQuWM3rDWV9Izs2jfh6U3WdBRXt7Pgk5bLzmB3zpD2+HO+jZcc0Of+SZFlbs3rbyf1xfCwul+1/Jb4es3tjuj0pveem98esrUmfs9Gk/yf3as3MxVl2jwMeJQYyAAAAQAm4BmtGhmdkAAAAABQOAxkAAAAAhcPSMgAAAKAkWFoGAAAAAH2MGRkAAACgBFzGjAwAAAAA9DMGMgAAAAAKh6VlAAAAQEk4S8seHTOrmtlVZnaTmd1oZt92XH6pmf2dmX3WzH66+7VRM/uwmd1gZteY2Z5etA0AAABA8fVqadmlkuTuz5P0FknveCgws5qkd0v6XknfLelyMztV0s9IutXdv1PS+7rlAAAAAJygjmxLPzZTTwYy7v7nki7vfvo4SdNr4vMk3enux9y9KelGSd8l6UJJ13W/51pJL+hF2wAAAAAUX8+ekXH3lpm9V9IPSPrhNdGUpJk1n89J2nbc1x/62rcws8vVHSSdeeaZJ7nVAAAAAIqgp69a5u4/LukcSVea2Xj3y7OSJtd826RWZ2zWfv2hrz1cnfvdfZ+779uzh8doAAAAAElylzpuW/qxmXoyI2Nmr5D0WHd/p6RFSZ3uhyR9SdLZZrZT0rxWl5X9hlaXoL1Y0s2SLpF0Qy/aBgwE9zirJH+/GBnO67X4BmVDye2kuRKX2zYVl2u346xajbNKkk2Mx5kkLSzGWXZ8svYk+69aLc6G63HW7sTZ2GicSdLiUpx1kr6T8aQ9oyNxlhw3O3IsLpcdtyzTOn0163PjY3GW9ZvkukmPTSarU5Jl/SO7P7RacbbB42aW3HOSc2Xr9cV1jsFGtpnu/wbvnWkrs2tRyo/5tsk4W27GWSfp48kx9z07wszmk/1Y5557yRmvC7Nr7/6ttCzQq6VlfybpD83sbyTVJL1e0g+Y2YS77zezN0r6uFZnhK5y93vN7H9Ieq+Z3SipKemyHrUNAAAAKKVBevnlngxk3H1B0o8m+cckfey4ry1K+pFetAcAAABAufT0GRkAAAAA6IWevWoZAAAAgM20+Q/cbyVmZAAAAAAUDgMZAAAAAIXD0jIAAACgJAbpVcuYkQEAAABQOMzIAAAAACXgEg/7AwAAAEA/YyADAAAAoHBYWgYAAACUgUvuW92IzcOMDAAAAIDCYUYGAAAAKImOBudhfwYyQAktP+WxYVZttDdc79DRhTDzkXpcMJnntlYnzmbmw6z1mJ1htnDGaJhNfCPeB0myHRNh1hmOb5mVpWZc50gt3WZog8eteepkWm39gWST1fgHYHaOrdkKs5VdY2FWO7IYN2Z8JG5LrRq3JTk2kuTbHhNm7bH4XA1NLyV1xvvYqcdtrSyvhFnWb9LrTVJlNmlr0o+z4+r1uFw1uzeMJm1tJ/cjy38Zy9paWYqPqyf1dsaH4w0ma1gqC40ws/n4XDTPOyOuVFJtejnMsv1Qth/J9eEjG/u10JL+6EP54p9K0h+B9bC0DAAAAEDhMAwGAAAASsAlOe8jAwAAAAD9ixkZAAAAoBRMHWZkAAAAAKB/MZABAAAAUDgsLQMAAABKInnl/tJhRgYAAABA4TAjAwAAAJQEL78MAAAAAH2MgQwAAACAwmFpGQAAAFAC7iwtAwAAAIC+xowMAAAAUBKdAZqRYSADFNQlj39jmNV3TYVZZXohrrTVyjc6OhJGdmg6Ljc8HGcLi3E2VI2jI/Nhti3J1n2B/eVGGFUqyQ+H5Nh4Pb7V2txSXOdwLc6WlsOo/o1mXE5Kz7ONj8bZkbm4zuRc1b+e7GOmGi8asEayj/XkuEmydjvMKslx1Y5tcZ1LcXsqKytxnUPJj+FKsv8LybmQ8mM3n+zjaHytVo7MxuXanXh72TWe7KOy602SteLzqJF4P9Lzf98DcbndO+PtJfcNdeJjU7/nWFxOSq9zs+T4ZFnSnuw+lu5jcl/17fHPI0myw0fTHMiwtAwAAABA4TAjAwAAAJTEegsPyoQZGQAAAACFw0AGAAAAQOGwtAwAAAAoCd5HBgAAAAD6GDMyAAAAQAm4jBkZAAAAAOhnDGQAAAAAFA5LywAAAICSGKC3kWFGBgAAAEDxMCMDAAAAlIHz8ssAAAAA0NcYyAAAAAAoHJaWAUU1vxhGlZVWXM6SKedmM92kzy/E1dZqcbnZ+bjSStweG4pvUVmdVk/a0kqOzXo68SOU1oiPnVWSvxkl7fHpOLNacvvOzrEkjQzH2bGZOKtW46wyEmdLS3GW9Bt5ctyWluNsvX6cHPOsH2t6Ns5GR5MNxv3GjxyL25Icb2804u1JUnLtpNfcUrL/nU7cnuW4PVavx3Um16oa+bXq7XYcziX3h9G4r2b3jvT8Z+Wye85CfB+XJM/uK+NjccHkfGTnPyvny/E1l/bVb9wTb0/K7yvYmAF62p8ZGQAAAACFw4wMAAAAUBI87A8AAAAAfYyBDAAAAIDCYWkZAAAAUBLJ64uUDjMyAAAAAHrOzCpm9h4z+6yZXW9mTzouv8TMPmdmf2tmv2eWvwwnMzIAAABACbj6/mH/l0kacffnmtkFkt4l6fslycwmJf1XSRe5+2Ezu0LSbkmHosqYkQEAAABwsuw2swNrPi5fk10o6TpJcvfPSdq3JvsOSbdKepeZ3SDpAXcPBzESMzIAAAAATp7D7r4vyKYkrX3n5baZDbl7S6uzL98j6RmS5iXdYGafdfc7og0xkAEAAADKwCX199KyWUmTaz6vdAcxknRE0t+5+0FJMrO/0eqgJhzIsLQMAAAAwGa4SdKLJan7jMyta7LPS/o2M9ttZkOSLpD0T1llzMgAAAAAJdHnL7/8EUkvNLPPSDJJrzKzN0q6090/amZvkvTx7vd+wN1vyypjIAMAAACg59y9I+m1x335y2vyP5X0pydaHwMZoIR8cSnMbHwsLtjp0Z9xKvF6XRsejst1OnGW/cmpWo23107qlNK2Kq52w231lVaYWb22oTo1khxTSWo042y94xPJ+k7Wnmw/kmOj8dE4W1qOM0k2Mb6hsum5Gk3ak7WlFv8Y9uZKXDDp41J+nfvCYlww66sbbI+322Fm7XiFe1ZOUtpXbSw5H9m1OrTBX4uSOtPjto70HtBKro/sPpa/LUdcbGRkQ22pZNcb8CgxkAEAAADKor+Xlp1UPOwPAAAAoHAYyAAAAAAoHJaWAQAAAKVg8v5+H5mTihkZAAAAAIXDjAwAAABQFjzsDwAAAAD9i4EMAAAAgMJhaRkAAABQBi4e9n+0zKxmZleb2Q1mdrOZfd+abK+ZXb/mY9rMXmur7l3z9Xf2om0AAAAAiq9XMzIvl3TE3V9hZjslfUHSRyXJ3Q9KukiSzOy5kt4h6UpJT5T0eXe/tEdtAgAAAMptgB7279VA5oOSPtT9v0lqHf8NZmaSfkfSv3X3tpmdL+l0M/u0pCVJb3D323vUPgAAAAAF1pOlZe4+7+5zZjap1QHNWx7m2y6V9MU1g5X7Jb3T3b9H0q9K+uOHq9vMLjezA2Z24NChQ71oPgAAAIA+17NXLTOzMyR9WtLV7v7+h/mWl0vav+bzA5L+jyS5+42STuvO2vwr7r7f3fe5+749e/b0oOUAAABAUdkWf2yeniwtM7NTJX1C0s+5+yeDb9sn6TNrPn+bpCOSft3Mni7pbncfoFV+wMljUxNx2GqHkTcaeb3jYxuq1yz+m0m2TavV0vaE6km5ajUvW0luwkvLcZa1tfUtq2u/yYbrG6uznZzHqfG4nCQ7Ercn/akwmfSrb/27079YWEzbE6okf2urrHMeEz6/EGY2MhJnY6NxpUNJezrJsels7Mdc2m8kKfnxme5H1ldHhje0PY0l943s+o9LrRrOjnknzkbjc+yz83G5DR7T7Lh5oxlvT5In13l6f8yyoQ3+6tdM2pr1jZXkfiOl9+RLznx9mF1712/m9WIg9OoZmTdL2iHprWb21u7XrpQ07u77zWyPpNnjBiq/JumPzewlWn2m5id61DYAAACgnAZoGqAnAxl3f52k1yX5IUnPOO5rxyS9pBftAQAAAFAuPXtGBgAAAAB6pVdLywAAAABstgFaWsaMDAAAAIDCYUYGAAAAKAOX5Jv7EshbiRkZAAAAAIXDQAYAAABA4bC0DAAAACiJQXo7eWZkAAAAABQOAxkAAAAAhcPSMgAAAKAsWFoGAAAAAP2LGRkAAACgLAbofWQYyAAF5Xt3hZk1WmHW2TMWl9u9Pd1mx+KbY2V+Kcx8tB5vc7ERZiun7wiz6nxczivxZHNlfjnMJKm1czzMho4thlnzlIkwqx1aiDdYjY9peh7HhsNs7uypeHuSpm5Ptrm8EhdMXgrn8PNOCbPtX46PW8ayl95Jsspy3MclqZlcA8P3zoZZe2IkzLK2enbdjMd1ZvvYqec/vq0Z9x2bS87HeHxsfDjeZnsy3o/qQjOu0+LrLdsHSVKtGtc7FN8DrJH08cnk/pgcN98WX/+d0VpcZztfA5Qdg6wPdJJjMzQd78fK7uR8dMJI1mzH2TovodWaiH8+1P/5/rQswNIyAAAAAIXDjAwAAABQEsbD/gAAAADQv5iRAQAAAMrAxcsvAwAAAEA/YyADAAAAoHBYWgYAAACUgg3U+8gwIwMAAACgcJiRAQAAAMqCh/0BAAAAoH8xkAEAAABQOCwtAwAAAMqCpWUAAAAA0L/CGRkz+0xSziR13P15J79JAAAAADZkgGZksqVlJunHkuxPTn5zAJyozuhwmPnUaJjZSieuc6yebrO62Iy3ORa3R51kmzsmwqz2jUPx9ibHwsyWV8KsnWxPkoaOLcbbrMavzV+bXk4qTSa/PfmJk2SVubidk1/LJ9vtyEyYdfbujMstxcd1+5eT4zYUH7fqQtynKkfnwqx1+q4wa09Uw0yShu+ajsN2O4w6o3HfGTq8EJfbEfdVLTXCyNrx+bel+HhLUvP07WFWn0/6asIarTAbWo7PVXb9azi556yzZsRmkz63azIpmLzHRiXeaNbnhg7F+29JnZZd/5LsWFyv79qWtGc6rrRWC6PqUnyObTm+Vm0h7sdqxXVKUq2ZnKup/H4NZAOZP5D0oLsvPVxoZi/vTZMAAAAAIJf9veOpkm41s98zs6cfH7r7V3rXLAAAAACPiEty29qPTRQOZNz99ZLOk/QpSb9qZjeZ2U+aWTJHDgAAAAC9l778sruvSPqQpA+Z2WmS/oOkuyTt3oS2AQAAAHgEjIf9/4WZjUj6AUmvlDQp6YpeNwoAAAAAMtnLL18k6cclfY+kP5f0H939tk1pFQAAAAAkshmZX5a0X9Jr3T15XT0AAAAAfYGlZZK7XyRJZlY1s5+U9DitPvh/m7sf3pzmAQAAAMC3WuftpiRJv6/VQcwLtfqMzPt62iIAAAAAWMeJDGSe6O6/JGnJ3T8mKX4rWQAAAADYBCcykBkys92SZGaTkjq9bRIAAAAA5NZ9+WVJb5F0k6THSPqcpNf3skEAAAAANob3kVnD3f9a0rlmtsfdD21CmwAAAAAgdSJviPkaSa+RNGJmkiR3f3KP2wUAAAAAoRNZWvY6SS+WdKzHbQHwCFQPHt1QOd8xGWb2jXVeWb2TzFdXbEPlbHQkLleNH+OzQ8ktqVaLq7x/nbfFGqrG21xcisuNjW6oPZqdizNLjmmisrS8oXKSVHlgY7f6oZlkPyrJ45hD8Y8h3zYRF/vawbjO9Y5blrdaYVS7qx1mPhGf/6H7kmO6shJny0lfTfqpJNW/PB+H2TU3PRNn2XnMeHL9Z321kz+S68n1aLOzcZb0OVXj4zp0MD7/mUp2jSfHRpJ8Je6PlXbSnixrxVl678j6XNZXt03FmSSbXYjDdY4PAr6xnx1FdCIDmX+UdLe7b+wKBgAAAICT7EQGMp+S9FUz+4okk+Tu/vzeNgsAAADAI+LdjwFxIgOZ10j6UUnTvW0KAAAAAJyYExnI3CPp79yd948BAAAA0BdOZCAzLOkfzOw2dSer3P2ynrYKAAAAwCPH0rJ/5Z09bwUAAAAAPALhQMbMrnP3i7tviBnmvWsaAAAAgEfCmJGRJD3fzO4LMpO0owftAQAAAIB1hQMZd69vZkMAAAAA4ESdyDMyAAAAAIpggJaWVba6AQAAAADwSD3iGRkzG3b3Ri8aAwAAAOBRYEZGMrP/veb/P78muranLQIAAACAdWQzMqes+f9LJL2r+3/rXXMArPWiiR8Ps8ope+KClfgytdmFuFx9ndf46HTizJJbQ7sdZ41kgjer0zf4J6dsHyRpaSXOhoeTcssby2q1OBtJttdO9mNpKc4kaSi59WfHtZKsRq7GmU+Nh5ktNeNsdjHeXqsVZ1k7JamV9MfsmCfXhy0n/WYhueayPp4c0/V4IzmuWcH0HFfj7S3HfdxGRjZWLuunkmwi7lfpPSfh2bWaye652TWeXceSbDi5Jyf76Mn1kR1XX4zvHWlbsn58dDrOJGk07h/ZcQWkE19atrYnDdCEFQAAAFAM5oP1PjLZn3s8+D8AAAAAbKlsRuYpZvZ+rc7GrP3/kzelZQAAAAAeGR+cJXnZQOZH1/z/PcH/AQAAAGDThQMZd//rzWwIAAAAAJyoR/w+MgAAAAD61AA92b7x13YEAAAAgC3CQAYAAABA4bC0DAAAACgJ3kfmUTKzmpldbWY3mNnNZvZ9x+VvMLMvmtn13Y9zzWzUzD7cLXONmSVvWw4AAABgkPVqadnLJR1x9++UdLGk3z0uP1/SK939ou7H7ZJ+RtKt3TLvk/SWHrUNAAAAKCff4o9N1KuBzAclvbX7f5PUOi4/X9KbzOxGM3tT92sXSrqu+/9rJb2gR20DAAAAUHA9eUbG3eclycwmJX1I3zq78qeS/rukWUkfMbOXSpqSNNPN5yRte7i6zexySZdL0plnnnnS2w4AAACg//XsVcvM7AxJn5Z0tbu/f83XTdJvuvthd29K+gtJz9TqoGay+22TkqYfrl533+/u+9x93549PEYDAAAASJJ89WH/rfzYTD2ZkTGzUyV9QtLPufsnj4unJN1mZudJWpD0fElXSVqU9GJJN0u6RNINvWgbUCSVM06Lw5XjV2yu0WjGmSd3mXZ7nQYlf/vItlmxOBsfj7OVlTjL9mNiLM5a6+yjJW2dm4+zej3OOp04qybHtJ2Uy47peoaqcZb1qw221RYbYeZjw3G5ucV4e7VanK0n68dJv/Lh+EemHTq2se1lWdYXs0ySjSRhdu1kWXKN21hyzSXXsVWTvrjOPqqZ3B/qSf9IjrkNJb8WJfvvnl3jyT5mx3u9sol0P5L7vI0mHWc8Ocezc3E2ORFnktRMfnYkf2+/eNurw+y6mavybaI0evXyy2+WtEPSW83soWdlrpQ07u77zezNWp2taUj6pLtfY2bXS3qvmd0oqSnpsh61DQAAACinAXr55V49I/M6Sa9L8qslXX3c1xYl/Ugv2gMAAACgXHr2jAwAAAAA9EqvlpYBAAAA2GwDtLSMGRkAAAAAhcOMDAAAAFASm/0SyFuJGRkAAAAAhcNABgAAAEDhMJABAAAAUDgMZAAAAAAUDg/7AwAAAGXBw/4AAAAA0L8YyAAAAAAoHJaWAQAAAGXgg/U+MgxkgH7WasdZLbl8mytxNjoSRp2p0bQ59s93xVlSr8bG4qwaTwx3tm0Ps8r8UlynWRi1d0/G5SRVlpJjN1ILI69V4zrnG3GdnU6crSRt8XgfPSsnyWrxfqid9LmpiXibyf7bctwea7SStiTHJmmLlpbjTJKGkmunFbennVwfQ+l+JMd0fiGMfM+OMLO5xbhOSb5zZ1w2OR/y+DcgT+451mjGdQ7X42w5uTYmkvuGJK0kx3xuPmnPcJxl18Z43B5L7mOai8+xJsbjTFJ7Z3y/qs7EfcCPHA0z2xn3K83OxVlyX0376qFjcZ3rlV2K+1XnyY9P68VgYGkZAAAAgMJhRgYAAAAoiwFaWsaMDAAAAIDCYUYGAAAAKAtmZAAAAACgfzGQAQAAAFA4LC0DAAAASsA0WO8jw4wMAAAAgMJhRgYAAAAoC2ZkAAAAAKB/MZABAAAAUDgsLQMAAADKwHnYHwAAAAD6GjMyAAAAQFkM0IwMAxmgn83Nh9HKOaeHWee0bXG5qfiyHznUyNvz1CeG0dDB6bg928bCbOmxE2E2et9imM099ZQwk8WRJ5kkmY+GWW2+HWYLe2th1q7HG526qxlmlWYnzBo74+2t90NsaDHeD4s3qZnH18Ns/MG4ztZIvP+jD67E5SaqYZadx8pKfgDG/vlImC2fc2qYZfu/+x/iA1c9PBdmjaeeGWbtkWz/t4eZJI0cXAqzpcdPhVl9Oj4ftWNxna1t8T3nwWeNh9mu2+J7Tms83n8p38f5b98bZvW5uK+mPO5Xtdn4uHVOie9xquQ3pPrBuO+0dsf1Vkfi+8Ph83eEWW0+vq9O3TETZp3ReHuL5zwhzKT8587Ms3aF2c6/j9uDwcHSMgAAAACFw4wMAAAAUBYDtLSMGRkAAAAAPWdmFTN7j5l91syuN7MnBd9zrZm9dr36mJEBAAAASqLPX375ZZJG3P25ZnaBpHdJ+v7jvuftkuKHudZgRgYAAADAZrhQ0nWS5O6fk7RvbWhmPyyp89D3rIeBDAAAAICTZbeZHVjzcfmabErS2peca5vZkCSZ2bdJukzSL53ohlhaBgAAAJTF1i8tO+zu+4JsVtLkms8r7t7q/v+Vkk6X9ClJZ0lqmtnX3T2cnWEgAwAAAGAz3CTpUkkf6D4jc+tDgbtf8dD/zeyXJR3MBjESAxkAAACgHFz9MCOT+YikF5rZZ7T69tWvMrM3SrrT3T/6SCtjIAMAAACg59y9I+n4l1X+8sN83y+fSH087A8AAACgcJiRAQAAAEqiz99H5qRiRgYAAABA4TCQAQAAAFA4LC0D+tmObWG0cPpImI0eXgmz+nScraeyHJftbBsLM2t1wmz8jqNxnePDYTb59/eFmUbico3HxsdUkobvnY3DVjuMFvaeGma7bpuP6/R4DUBluRVnzbgtj+YVa6oLjTDbPVuL27MU943Fs6bCzNpx36guW5gNzcfb86H8b3TtneNhtrwz/rFYW0gObDvOsmuj/uBCXGdy3VjSb9Yz/rXkHpBsM/vT59DB6TDb+5m4r9pyM8xqw3F/k6TK3HKYTX4l3mZlPu7jqsR9rj05Gma2kuyjx32qOr0Yt0WSkusj6+fWjO8duz99T7w9i+tsnxJfx9WjcT+emI/PkyTZdHx/3DU9GWZerab1DjSWlgEAAABA/2JGBgAAACgJHvYHAAAAgD7GQAYAAABA4bC0DAAAACgLlpYBAAAAQP9iRgYAAAAoAxczMgAAAADQzxjIAAAAACgclpYBAAAAJWDdj0HBjAwAAACAwmFGBgAAACgLHvYHAAAAgP7FQAYAAABA4bC0DOhny40wGr8vzqoLzQ1tzhoreb6c5ElbVU3+ZlKrhVHlgWNxueF6GPlQvL36gwtxnevpdMJox20zSbk4Mo/XANj8UphVV9obqlOS1E4atJhsc2w0zHw0Ph+j9y2GWdbnbDHpU/W432i9/U/UFuJ9HPt6fI7TayPpNz4Rb88ayXVcWefvkK2kf8y34nKN+Jj7KTvjcivx/lfuOxyXS9joSP4NyT2nspLsY3Y/WoqPeXV6Ni6XnGMNJb9qbZuMMyndx9qh+bhcdo1nx2ZyPIyqR5LtJX3VFpK2SGnfsSPJNVet5vUOMGNpGQAAAAD0L2ZkAAAAgLJgRgYAAAAA+hcDGQAAAACFw9IyAAAAoCxYWgYAAAAA/YsZGQAAAKAMnJdfBgAAAIC+dtJnZMysJukqSWdJGpb0dnf/6Jr830h6vaSWpFsl/ay7d8zs85Ieerepr7n7q0522wAAAACUQy+Wlr1c0hF3f4WZ7ZT0BUkflSQzG5X0dklPdfdFM/sTSS81s09IMne/qAftAQAAAAYDS8selQ9Kemv3/6bVmZeHNCR9h7svdj8fkrQs6emSxszsE2b2KTO7oAftAgAAAFASJ30g4+7z7j5nZpOSPiTpLWuyjrs/IElm9u8lTUj6S0mLkn5D0oskvVbS/zKzh50tMrPLzeyAmR04dOjQyW4+AAAAgALoyauWmdkZkj4i6ffc/f3HZRVJvy7pHEk/5O5uZndIutPdXdIdZnZE0mMk3X183e6+X9J+Sdq3b98ATZ4BAAAAuUF61bJePOx/qqRPSPo5d//kw3zL72t1idnL3L3T/dqrJT1V0s+a2WmSpiTdf7LbBpTJ0NGFMPPhWphV5pbjStvtfKOVZBK3Gmc+ORZmtrwS12kWZ41mvL3t43GVK+vsY6bVCqPKYtyelT0TYVa763C8vWT/bS4+/5qIj7ck+VA1rteTn4DZOa7FdarTCSOvJm2px/1YrY2fR6/FP/rGvjYdtyfrq+14H7N+Y4vJNdWMt+eLi2EmSTYRXwNKzrEn9wBrxPuRXqvJfcOXluIqh4fjOqV0P5TUq+F6Xu8GyvnsfJjZUPKrVlJOUtp3dCzpj9mxybKsPcl1rFpyrWZ9Q5LGk76a9fOsPRgYvZiRebOkHZLeamYPPStzpaRxSQck/aSkGyR9ylY7929J+p+S/sjMbtTqI0qvdvfk6gUAAADwLZiR2Th3f52k1yXfEv1p5rKT3RYAAAAA5cQbYgIAAAAonJ487A8AAABg8w3Sw/7MyAAAAAAoHGZkAAAAgDJwDdTD/szIAAAAACgcBjIAAAAACoelZQAAAEBZsLQMAAAAAPoXMzIAAABACZh4+WUAAAAA6GsMZAAAAAAUDkvLAAAAgLIYoKVlDGSAPuZT43FoFpcbSiZbFxbjctsm0/ZYcyUOa7W4XKMVl2t34qzZjLPRkTDK9t9ayfYkyZOfAGOjcbFqfD5qdx2Oy42PhZmtJMetGu9Hev4l2fxyEsb70Rkfjos14r6RHXOvVeO2ZOdiuRFnK0k/leR7d6d5xFpLyTaTc5X0VS0mdVaSfjwcnwtJUic5dsm1mt0ftJT0m0xyHtP9aCXHVJI6Sb9Kjqsl5bJrPOtzllzHWTvVbseZlJ+rrJ9Xk+sqa08t+bWwkdyPszqTe4ok+bHpuOjkxIa2efG2V4fZdTNXpe1BsTCQAQAAAErCsj8ClQzPyAAAAAAoHAYyAAAAAAqHpWUAAABAGbgG6mF/ZmQAAAAAFA4zMgAAAEBJGDMyAAAAANC/GMgAAAAAKByWlgEAAABlwdIyAAAAAOhfDGQAAAAAFA5LywAAAICS4FXLAAAAAKCPMSMDAAAAlMUAzcgwkAH6WGe4Fmbt8Tir33MszFaecGqY1e4+kjfIk7vj6EhcLNmPrE5bWIjLDcW3L69ufLK5Mz4cZpW55bjctvG40qzOxWaYeTU5bmYbyyQpOx8bPHbWaIWZD1XDrLUt7jeVlU6YVTtJX6wn+yelaxG8nvSrmbkws4n4/PtI3B5bXIrLzcf933ZuDzNJUjU+5lqJz5VaSZacRx8bDTNLjlvaVzvx+Zck1ZLjOpKUTcppfjHOsn7VjK/jR3WtJvdHb67E1daTOrO+0U6O23BSaXL+lZ1/SZYc12vv+s0wu+SsN4SZP+GMdJsoD5aWAQAAACgcZmQAAACAMnAe9gcAAACAvsaMDAAAAFAWzMgAAAAAQP9iIAMAAACgcFhaBgAAAJSAiYf9AQAAAKCvMSMDAAAAlEX25tUlw4wMAAAAgMJhIAMAAACgcFhaBgAAAJQED/sDAAAAQB9jRgYAAAAoA+9+DAgGMsAWe9ar/luY7ex0wuzYuSNhNr5tT5iN3bsYZp3d28JMkjrD8S2j0miFWWuiHmbWjvfRd5wRZkMLK2F25GnjYbbzn/KJ6Nqh+TDrjA2H2dLe0TCb+OKhMGvvmggza8XHprE73t7o7Q+GmSS19m4Ps+piM8yy89/ZMxlmjZ3x+R/7ynSYaSg+V9m5WHxcfEwlaeTBRhxWLIxsb3xdKenHqank/FerYdbZke9jezQ+V7W7j8Tb3B7fA5qP2x1m9Qfmwsy3xX3DFpficpNjYSZJthT3Vd+eHJ9K3K9sOt4PjcXXXPoqUa12nK1nKO4DNhr/DNBy0seTtnb27oy3t5Lsxz0H43K74jolqb0jPs+XnH1FmF379XfH5c54XbpNlAdLywAAAAAUDjMyAAAAQEnYBieIi4gZGQAAAACFw0AGAAAAQOGwtAwAAAAoiwF61TJmZAAAAAAUDjMyAAAAQEkYMzIAAAAA0L8YyAAAAAAoHJaWAQAAAGXgknxw1pYxIwMAAACgcJiRAQAAAEqCh/0BAAAAoI8xkAEAAABQOCwtA7bYoWd3wmx517Ywa8aRFk+NL+09Nh5mrVGLK5VUWYkz68Rz2YefWg2znV+K939lLG7P+MF4H+tzcVsOPWM0zCSpuhzn7eG4XHY+jjx5b5iNHYzbWk2O945/nA6z+y49PS64jvH74/1fPDX+29fyzrjOrN8MP3Z3mFk7LnfKjYfDrFPL/0bX3FEPs9mzamG24464Hzcn4/44enA5zDojcZ2N7bvCrD2cX6vVRra2JK539vHx+Z+8qxGX+7b4PI4+mJR7fNyWoaV8fczQcnzvqC4l99Xd8bkav3ckzCqNVph5JelzyanqDMfnX5Jmz4pvOtvvWEras7FtziTbm7wnvpBXzj43zCqt/DxWl+ILffmJ8c+ri5/6i2F28GVnpdssPZaWAQAAAED/YkYGAAAAKAETD/sDAAAAQF9jIAMAAACgcFhaBgAAAJSB++rHgGBGBgAAAEDhMCMDAAAAlAQP+wMAAABAH+vJQMbMamZ2tZndYGY3m9n3HZdfamZ/Z2afNbOf7n5t1Mw+3C1zjZnt6UXbAAAAABRfr2ZkXi7piLt/p6SLJf3uQ4GZ1SS9W9L3SvpuSZeb2amSfkbSrd0y75P0lh61DQAAACgn3+KPTdSrgcwHJb21+3+T1FqTnSfpTnc/5u5NSTdK+i5JF0q6rvs910p6QY/aBgAAAKDgevKwv7vPS5KZTUr6kP717MqUpJk1n89J2nbc1x/62rcws8slXS5JZ5555kltNwAAAFBkPOx/EpjZGZI+Lelqd3//mmhW0uSazyclTR/39Ye+9i3cfb+773P3fXv28BgNAAAAMIh69bD/qZI+IekX3P2q4+IvSTrbzHaaWV2ry8o+K+kmSS/ufs8lkm7oRdsAAAAAFF+v3kfmzZJ2SHqrmT30rMyVksbdfb+ZvVHSx7U6kLrK3e81s/8h6b1mdqOkpqTLetQ2oK884UOtMFs4Lf5bQ23OwmzynpUwa26rhtnI4bgtklRZ6cTtObYUZ3PjSXvi29COL82HmTXaYTZ3xvYwm7w7LidJnVp8XLOHGGuLcbnh6fi4VZLmtOtxnT4U940dd8TnX5KqS/F5bu6oh9nuf4zP8dDRhTBbPn0qzLwS72O2PGLhnB1hVp/N+/H8abUwm7g3Ljt813SYDW0bC7P2RLy9odlGnM0sh5kq+d8hvZr1nfgesPPzR5Jy8bU6MRfvR2tqOMx23XQw3l49Pm6StHxG3K+G5uNrYPs/3Rtm7cfujtuTHLehuw6FmTrx9d85Ne7HkrT9jrhsdTa+Hq0Vl2ttj/vqrtvi4+bVuM/Vj8R9tT2W/6pZacZtHT4c76Ms7uOnfnYmzErPJXUGZ21Zr56ReZ2k1yX5xyR97LivLUr6kV60BwAAAEC58IaYAAAAAAqnV0vLAAAAAGy2wVlZxowMAAAAgOJhRgYAAAAoCd5HBgAAAAD6GAMZAAAAAIXD0jIAAACgLHxw1pYxIwMAAACgcJiRAQAAAEqCh/0BAAAAoI8xkAEAAABQOCwtAwAAAMrAux8DgoEMsMWs3Qmzqa8shllzez3MViarYVafaYdZdbkVZpI0NLMcZu3x4XibR+L9GD4Yt6czEu+jteNytYX4mA4fWwkzSbJO/BPAWnG9o4fiY55vMI5qR5fCrD0RH+/qUn4ea0fj85Gtra404nq9Hv84qTbi45apLsTnqtKIs+Yp42m9o0fivjN8tBkXTF4JKO2PDzTiKkfi41Y5Ohe3ZSQ+/5KkpB9n++HDSXumk/YMxeXqixs7pqomF4ek4UNJP16Jz4eG4mu1shCfK1tK9qNei7PlpM717rnZsVtJyibnf+hQUi49H8k9rhHvY7WWHJv1trkU/8xRLe5zlpS75Kw3hNm1X393vD30JQYyAAAAQAmYJOvjl182s4qk35P0dEkNST/l7neuyd8g6ce6n17j7r+S1cczMgAAAAA2w8skjbj7cyX9J0nveigwsydI+reSvkPSBZK+18yellXGjAwAAACAk2W3mR1Y8/l+d9/f/f+Fkq6TJHf/nJntW/N9d0u62N3bkmRmNUnJ+kIGMgAAAEB5bOyxxJPpsLvvC7IpSTNrPm+b2ZC7t9x9RdJhMzNJ/1XS37v7HdmGWFoGAAAAYDPMSppc83nF3b/5ChRmNiLpf3W/52fXq4yBDAAAAFAS5r6lH+u4SdKLJcnMLpB06zfbvToT838k/YO7v+ahJWYZlpYBAAAA2AwfkfRCM/uMVl9k7VVm9kZJd0qqSvpuScNmdkn3+9/k7p+NKmMgAwAAAKDn3L0j6bXHffnLa/4/8kjqYyADAAAAlIF3PwYEz8gAAAAAKBxmZAAAAIBScGn9B+5LgxkZAAAAAIXDQAYAAABA4bC0DAAAACgJG5yVZQxkgK1WWemEWXNHPcyGFlpxthS/h5R14jvc0PRSmEmSTc/H2XAtKWhxtLwSZtX55bjOoWoYTX4t2Y9K3Jb1VBrxMe8Mx+1RdsznmnG5dnIeW3E2tBz3KUlSJZ6Mrx2cDbPO1Ghc5cximFVr8bGpLMXnX614P2ypEWbt0yfDTJJqs1mfi+vNpPvRjDNLMlmyaKKRlJPS6yNbP29LSX/M1t03knLLyTEdju9x9uDRuJwkq2bXXHIN1OJ7Vbr/yX0s3cfkerPp+Hpbr2wqO//ZPo4Mb2x77eR4D63zW/X8QlI2+TU1Ox/j43HWTPYfhcPSMgAAAACFw4wMAAAAUBa8ahkAAAAA9C9mZAAAAIAycMnWeUyyTJiRAQAAAFA4DGQAAAAAFA5LywAAAICy4GF/AAAAAOhfzMgAAAAAZTE4EzLMyAAAAAAoHgYyAAAAAAqHpWUAAABASRgP+wMAAABA/2JGBgAAACiLAZqRYSADbLG/vOktYfbCC98RZp16PKHaGqmG2eh982Hm9fyWYKMjcXtG47JDxxbjSqvJxHCrvaGsNVELs/qx5bhOSa2JephVFuIfDpVmJ8yqM/H+d8aHk+3FdVYWm2FmjVaYSZIWl8LId0zG25yNy6keH/PK0kq8vVrcV80s3l4r7m/WeRQ/xLNfAJaSvpOVm5qIs5XkXA3Hx1SN+JhKkppx/0jV4/6vdtwf1Ur2Yyi5rzSSdmblpPy4Hp2Os3ZyX1lO9nElOeZZW5P7puYacbZe2ez+2EzaWknuuQvJvXrHtjjLZNeNJI2NhZFPz4SZVeN7hybiOjWeZCgclpYBAAAAKBxmZAAAAIAycEnJxGLZMCMDAAAAoHCYkQEAAABKwOS8/DIAAAAA9DMGMgAAAAAKh6VlAAAAQFmwtAwAAAAA+hcDGQAAAACFw9IyAAAAoCxYWgYAAAAA/YsZGQAAAKAMXFJnqxuxeZiRAQAAAFA4DGQAAAAAFA5Ly4At9sLveHuYzZ81GmatEQuznf84G2Y+FP/9wmvVMFtt0EIYrYzvDLOhmeRvJu2VMOpsGw8za7XickPxsbFmXE6SqgtJ2XY7qTfOOuPDYVZZaMTlRupxueVmmLV2TYSZJFXG4vakkj7XzvZxKT7HlmSdqZEwqx6N+/jwocUwkySvxv3cjs3F7dm9Pcwqh6fjDS4tJ41JHsptJ+tDhta5VleSsvW4X6kZ9yuNxudDC8kxH06210n2fyXuG5Kkufh+pLGxjdU7klwb2TGvJFl2/sfje9y626wlx26jfS7b/+SeI4vvDeqss84puc9Zcq1q9444W47vq+1tSd8oCeNhfwAAAADoX8zIAAAAAGXBjAwAAAAA9C8GMgAAAAAKh6VlAAAAQCk4S8sAAAAAoJ8xIwMAAACUgYsZGQAAAADoZz0byJjZc8zs+uO+ttfMrl/zMW1mr7VV9675+jt71S4AAAAAxdeTpWVmdoWkV0j6V2+76+4HJV3U/Z7nSnqHpCslPVHS59390l60BwAAABgIna1uwObp1YzMVyT9YBSamUn6HUk/4+5tSedLOt3MPm1m15jZuT1qFwAAAIAS6MmMjLt/2MzOSr7lUklfdPfbu5/fL+md7v5BM7tQ0h9LetbDFTSzyyVdLklnnnnmyWs0AAAAUHDGw/4993JJ+9d8fkDS/5Ekd79R0mndWZtv4e773X2fu+/bs2dP71sKAAAAoO9s1UBmn6TPrPn8bZJeL0lm9nRJd7sP0HASAAAAwCOyKe8jY2aXSZpw9/1mtkfS7HEDlV+T9Mdm9hJJLUk/sRntAvqB1+K/J3z2/T8fZi983tvD7OMHfjnMXvBd7wizykr+hGD7zFPCbOTBxTDzh59glSQ1z9gRZp1aXG7k/vk4O7QUZguPnwozSZr44qEwWzx7d5j99TVXhFl2rpqnTIbZ8P2zcbm98X7UjsX7L0ntsXqYLTx2NMym7pgJs+piM8x8KO7jnrSlU6+GWWVnvP8r2+N9kKTabCPMWmfE57g6H5drn74rLjcTn4+V5PwPzSzH20uOmyQN3fVgXHbP9jCrHm2HWWfbWJhVluNj03hcfGyyPt7ZNh5mkuS1pH8sxe3pjMd9p3p3fP1rPN7/zlTc5ypH4vuqT8V1SpLNxPe5zva479ho3D9sKb5Ws/cf8bHhuM75pK/u3RlvT1L16Fwc7knKzsbHRpNx32nsHknbUwoDNBfQs4GMu39d0gXd/79/zdcPSXrGcd97TNJLetUWAAAAAOWyKTMyAAAAAHrMJXUGZ0Zmq56RAQAAAIANYyADAAAAoHBYWgYAAACUgg/Uw/7MyAAAAAAoHAYyAAAAAAqHpWUAAABAWbC0DAAAAAD6FzMyAAAAQFkwIwMAAAAA/YuBDAAAAIDCYWkZAAAAUAYuqTM4S8sYyABbrLGzHmbPvexdYbb0zPEwu/CHfiPM5p45FmZjD3bCTJImv7YQt+e0uD3t4Xjytz7bCjOvWpgtnzYZZs3JaphNn5NPRDe27Q2ziXuaYfa9z/3PYdaais/xykTc1sXH7A6z8XuWw+zY07aHmSQ1puLjuuu2pTCbf+JUmD3wrHg/zvhkI8xaY3G5zMpULQ7X+SG++Ji472z70kyYzT9pW5iNHI77xr3ff0qYLe2N23rq3w7H2zsaXzeS1D7vtDCrHYv7Tmd7fB2rE98f2nt3htn8Y+P+70PxMX3wmXE5SWqPxtmeL8T3uZEHkv0/Lb7mDj8j7v+jR9vx9sbivtoezX8Nm33WjjBb2hOXW3hC3D/OuDa+/hvb4vvj1Nfi63j+GfH5HzkWHxtJ8sfEJ7K6FPc5r24Ps6GFlTB74Dnx+ch+dqI/MZABAAAASsElz/8oWSY8IwMAAACgcBjIAAAAACgclpYBAAAAZcH7yAAAAABA/2JGBgAAACiDAXv5ZWZkAAAAABQOAxkAAAAAhcPSMgAAAKAseNgfAAAAAPoXMzIAAABAWTAjAwAAAAD9i4EMAAAAgMJhaRkAAABQCj5QS8sYyABbbGU8nhg9dq6F2e1vfcOGtveqv3tVmH3l7U9Oyx59ykSYeSVu6y1Xbqyt57zj3WFm7bjc7b+0se2t58l//sthdtsF7w+zJ/3Ja8Psq29444ba8oT3/2pc52V5nd/+mvi4fvL6N2+oPZkXffBtYfapv/yVk769J33g7Wl+549ecdK3mbn73seE2Rmn39+TbXYOnhNmL3nGC8Ls2oO/t6HtXXTxfwmznQeOhNl1X3zHhra33javv+4XwuzZP/HfwuzmP9rY9Zi5+Cm/GGZ/9Sj2f8N+anM3d9bv/0aaT9xZDbMLf+zzYfae868Os6e/Lr7Hfflt8c+Hx//uu8JMfxZH2DoMZAAAAIAycEmdzla3YtPwjAwAAACAwmEgAwAAAKBwWFoGAAAAlMUAPezPjAwAAACAwmEgAwAAAKBwWFoGAAAAlAVLywAAAACgfzEjAwAAAJSCSx1mZAAAAACgbzGQAQAAAFA4LC0DAAAAysAl985Wt2LTMCMDAAAAoHCYkQEAAADKYoAe9jcv8GtN79u3zw8cOLDVzQDQZx531a+H2TdefUVPtvnEd/23MPvKz7+xJ9sENtMzfu7dYfaF333DSd/eU/5TvD1J+uKvnfxtAhEzu8Xd9211O9azbWiPP3fqZVvaho8f+4NNO1YsLQMAAABQOCwtAwAAAMqiwKutHilmZAAAAAAUDjMyAAAAQBm4Sx1efhkAAAAA+hYDGQAAAACFw9IyAAAAoCx42B8AAAAA+hczMgAAAEBJOA/7AwAAAED/YiADAAAAoHBYWgYAAACUgvOwPwAAAAD0MwYyAAAAAArHvMDTT/v27fMDBw5sdTMAAABQYmZ2i7vv2+p2rGdbZZdfMPziLW3DJ5b/eNOOFTMyAAAAAAqHh/0BAACAsnDeRwYAAAAA+hYDGQAAAACFw9IyAAAAoARckneK+0JejxQzMgAAAAAKhxkZAAAAoAzcedgfAAAAAPoZAxkAAAAAhdOzgYyZPcfMrn+Yr7/BzL5oZtd3P841s1Ez+7CZ3WBm15jZnl61CwAAACgr7/iWfmymngxkzOwKSX8gaeRh4vMlvdLdL+p+3C7pZyTd6u7fKel9kt7Si3YBAAAAKIdezch8RdIPBtn5kt5kZjea2Zu6X7tQ0nXd/18r6QVRxWZ2uZkdMLMDhw4dOmkNBgAAAArPO1v7kTCzipm9x8w+212Z9aTj8p/u/p7/OTN76Xq72pOBjLt/WNJKEP+ppNdKer6kC7uNnJI0083nJG1L6t7v7vvcfd+ePaxAAwAAAAriZZJG3P25kv6TpHc9FJjZXkn/QdLzJL1I0jvNbDirbFMf9jczk/Sb7n7Y3ZuS/kLSMyXNSprsftukpOnNbBcAAACAnvvmKix3/5ykfWuyZ0u6yd0b7j4j6U5JT8sq2+z3kZmSdJuZnSdpQauzMldJWpT0Ykk3S7pE0g0nUtktt9wyb2a396itKK/dkg5vdSNQSPQdbAT9BhtF3+kfj9vqBpyIOR37+F/5h3ZvcTNGzOzAms/3u/v+7v/XrsKSpLaZDbl762GydJWWtEkDGTO7TNKEu+83szdL+rSkhqRPuvs13Vc3e6+Z3SipKemyE6z6dnfft/63Af/CzA7Qb7AR9B1sBP0GG0XfwSPl7hdvdRvWsXYVliRVuoOYh8vWXaXVs4GMu39d0gXd/79/zdevlnT1cd+7KOlHetUWAAAAAFvuJkmXSvqAmV0g6dY12c2S3mFmI5KGJZ0n6basss1eWgYAAABgMH1E0gvN7DOSTNKrzOyNku5094+a2W9r9RGTiqRfdPflrLKiD2T2r/8twLeg32Cj6DvYCPoNNoq+g1Jx945WX714rS+vya+UdOWJ1mfum/sOnAAAAADwaG3qyy8DAAAAwMnAQAYAAABA4fT9QMbMKmb2HjP7rJldb2ZPOi7/aTM7YGafM7OXblU70X9OoO/8lpnd0s2uN7P0tcoxWMzsOd2Xhj/+65ea2d91+9VPb0HT0MeSfvMGM/vimvvNuVvQPPQhM6uZ2dVmdoOZ3Wxm33dczj0HCBThYf+XSRpx9+d2X6btXZK+X5LMbK+k/6DVdwUdkXSjmf2luze2qrHoKy9T0He6zpf0Infnzcbwr5jZFZJeodU37l379Zqkd0t6Vje7ycw+6u4PbH4r0W+iftN1vqRXuvstm9sqFMDLJR1x91eY2U5JX5D0UYl7DrCevp+RkXShpOskyd0/p9VBy0OeLekmd2+4+4ykOyU9bfObiD4V9h0zq0g6W9J+M7vJzF69NU1En/qKpB98mK+fp9WXiDzm7k1JN0r6rk1tGfpZ1G+k1YHMm8zsRjN70ya2Cf3vg5Le2v2/SWqtybjnAIkiDGSmJM2s+bxtZkNBNieJ5UF4SNZ3xiX9jlb/EnaxpJ81MwbBkCS5+4clrTxMxD0HoaTfSNKfavUlR58v6UKWQuMh7j7v7nNmNinpQ5LesibmngMkijCQmZU0uebziru3gmxS0vQmtQv9L+s7i5J+y90X3X1O0qckPX2zG4jC4Z6DR8zMTNJvuvvh7l/V/0LSM7e4WegjZnaGpE9Lutrd378m4p4DJIowkLlJ0oslqfucw61rspslfaeZjXQf1D5P0m2b30T0qazvnKPVtcbV7hrkCyV9fvObiIL5kqSzzWynmdW1usTjs1vcJvS/KUm3mdlEd1DzfEk8KwNJkpmdKukTkn7B3a86LuaeAySK8LD/RyS90Mw+o9W1o68yszdqdc3oR83styXdoNVB2S+6+/IWthX9Zb2+c7Wkz2l1Kcj73P2LW9hW9DEzu0zShLvv7/ahj2v1nnOVu9+7ta1Dvzqu37xZq39xb0j6pLtfs7WtQx95s6Qdkt5qZg89K3OlpHHuOUDO3H2r2wAAAAAAj0gRlpYBAAAAwL/CQAYAAABA4TCQAQAAAFA4DGQAAAAAFA4DGQAAAACFw0AGAArCzM4ysz/q/r9mZv+fmd1sZteb2V+a2XPWfO/1D1O+aWafWPP5k8zs1uO+5wfN7NXd/4+Y2UEz+49r8rd3v3bxyd9DAABOHAMZACimX5M0IukCd79I0k9J+p9m9vikzFF3/15JMrNXSPpTSXuO+54Xa/Wd5yXph7rf8xNmVpEkd3+LpOtO1k4AALBRDGQAoGDMrCbpRyW92d07kuTu35D0u5J+4gSrOSbpu4+r1ySd4u4PdL/0U5L+UNI/aHWAAwBA32AgAwDFs1ursyut477+dUlnnUgF7v5/3X3huC8/S9IBSTKzs7X6zuL/IOkqSf/u0TQYAICTbWirGwAAeMQOS9plZkPHDWbOkXTvo6j3pZL+vPv/n5I0bmbXSTJJ32FmT3L3Ox9F/QAAnDTMyABAwbj7iqQPSHqHmVXM7PVm9tuS/r1Wl4Jt1DPd/fPdpWs/Juk73f1id3+RVp/J+dlH3XgAAE4SBjIAUExXSGpI+qykH5H07ZLuk/SUjVRmZqd1y0vSpZJucfeja77lDyW9wszGNtxiAABOIpaWAUABdZeU/VL3Q9LqyyVLevIjrGdv99/7JL2m+/8/k/Rnx33ffeq+wtnqawIAALC1mJEBgJJw92V3/3zyLTvXvo/MRpjZ2yXxHjIAgC1n7r7VbQAAAACAR4QZGQAAAACFw0AGAAAAQOEwkAEAAABQOAxkAAAAABQOAxkAAAAAhfP/A4fZZy87qvd0AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"ax = ds.backgroundModel.plotPowderAverage()\n",
"ax.set_clim(0,1e-7)\n",
"ax.get_figure().set_size_inches(15,12)"
]
},
{
"cell_type": "markdown",
"id": "25e74d59",
"metadata": {},
"source": [
"This is all good and great, but unless we invoke the background subtraction for specific cuts, we haven't actually utilized it! Let's perform a QE plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a993e086",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:15.038985Z",
"iopub.status.busy": "2023-07-25T11:36:15.038985Z",
"iopub.status.idle": "2023-07-25T11:36:15.724028Z",
"shell.execute_reply": "2023-07-25T11:36:15.724028Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"Q1 = np.array([1.0,-0.5,0])\n",
"Q2 = np.array([1.0,1.5,0])\n",
"EMin = 1.6\n",
"EMax = 3.7\n",
"dE = 0.1\n",
"\n",
"width = 0.05\n",
"minPixel = 0.02\n",
"\n",
"# First the full data is plotted\n",
"ax,*_data = ds.plotCutQE(q1 = Q1, q2 = Q2, EMin = EMin, EMax = EMax, dE = dE,\n",
" width = width, minPixel = minPixel, colorbar = True, backgroundSubtraction=False)\n",
"ax.set_clim(0,3e-7)\n",
"ax.get_figure().set_size_inches(15,12)"
]
},
{
"cell_type": "markdown",
"id": "a5e39c1f",
"metadata": {},
"source": [
"The above figure is the regular cut *not* using the background subtraction. If one adds the backgroundSubtraction=True"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f3269d3a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:15.727027Z",
"iopub.status.busy": "2023-07-25T11:36:15.727027Z",
"iopub.status.idle": "2023-07-25T11:36:16.467698Z",
"shell.execute_reply": "2023-07-25T11:36:16.466870Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"axSub,_dataSub, bins = ds.plotCutQE(q1 = Q1, q2 = Q2, EMin = EMin, EMax = EMax, dE = dE,\n",
" width = width, minPixel = minPixel,\n",
" backgroundSubtraction=True, colorbar = True)\n",
"\n",
"\n",
"axSub.set_clim(0,3e-7)\n",
"axSub.get_figure().set_size_inches(15,12)"
]
},
{
"cell_type": "markdown",
"id": "10ee3dd5",
"metadata": {},
"source": [
"Some difference between the two above figures is visible but unless one is to do 1D cuts it is not too different. The background subtraction has been implemented for all of the cutting functions. For the plotCut1D and plotCut1DE two additional keyword arguments have been added allowing for a direct plotting of the also the unsubtracted and the background signal with correct errorbars"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c5e62aed",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:16.469708Z",
"iopub.status.busy": "2023-07-25T11:36:16.469708Z",
"iopub.status.idle": "2023-07-25T11:36:16.869515Z",
"shell.execute_reply": "2023-07-25T11:36:16.869515Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"Emin1D = 3.0\n",
"Emax1D = 3.1\n",
"\n",
"# Either plot using two different calls, and a third for the background\n",
"ax1D,data1Dsub,bins = ds.plotCut1D(q1 = Q1, q2 = Q2, width = width, minPixel = minPixel,\n",
" Emin = Emin1D, Emax = Emax1D, backgroundSubtraction = True,\n",
" label = 'Subtracted', plotForeground=True, plotBackground=True)\n",
"ax1D.grid(True)\n",
"ax1D.legend()\n",
"ax1D.get_figure().set_size_inches(14,9)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "26311ddf",
"metadata": {
"execution": {
"iopub.execute_input": "2023-07-25T11:36:16.872479Z",
"iopub.status.busy": "2023-07-25T11:36:16.872479Z",
"iopub.status.idle": "2023-07-25T11:36:17.247108Z",
"shell.execute_reply": "2023-07-25T11:36:17.246394Z"
}
},
"outputs": [
{
"data": {
"image/png": 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77bcD8MwzzzBnzhwWLFjAH/7hHzJ69GjWrl3Lu971Lp555hlmz55NbW0ttbW1fPnLX6arq4uf/OQnjB07lm3btgGwYsWKPfcbOXIks2b9usYTTzwRKIxut7S0UF9fD0BrayvLly9/VV0xxlddPuWUUwCYNGkSPT09rFu3jtmzZwNw3HHHMWnSpGE/F+AIuCRVpOXrNrN83easy5AkSTkyY8IMThx3IhNHT+Sms29ixoQZJd1+U1MTAJdeeikLFizg1ltv5Y1vfCMxRqZOncqPf/xjBgYG6O3t5b3vfS89PT3MnDmTe+65h9tuu42f/vSnTJ8+nccff5yBgQF6enpetahbTU0h3k6dOpXHHnuMvr4+Yow8+OCDvOUtb2HEiBF7Rsx//OMfv6q2EMKrLp900kk8+uijALzwwgusXbu2JM+BI+CSJEmSJABGN4xmdMPokoXv3VPQa2tr2bp1K1/5ylf46U9/SmtrK6NGjaKpqYkXXniBGTNmcM455/Cud72LgYEBPvWpT3HYYYcBMHLkSL761a/yiU98gscee4wPfOADtLS0MH78eOrr6/eMdO/21re+lXPPPXfPtk4//XR+4zd+g66uLs4991weeOABZs6cecC6P/zhD/PDH/6Q0047jcmTJzN+/PiSPB8GcEmSJElSybW1tbFhw4Yh3//KK6/kyiuvfNV1HR0dAJx++uksW7aMDRs2MG7cODo7O+np6WH69OlMmjSJz33uc6/6uSuuuIIrrrjiVdcdf/zxPP7446973K6urj3nr732WrZu3UoIgX/5l38Zcu1DZQCXJJWX3ce+nn9PtnVIklSFFpyzIOsSDmj8+PE8/vjjnHrqqYQQuPTSSznuuOOyLmvIDOCSJEmSpFyoqalhwYLy/pLgQFyETZIkSZKkFKQ6Ah5C+DGwpXhxVYxxfpqPL0mSJEnVJMb4uhW+VRqvPZTZUKQWwEMII4AQY2xL6zElSZIkqVqNGDGCjRs30tjYaAgvsRgjGzduZMSIEQf1c2mOgL8dODyEcH/xca+KMXak+PiSJEmSVDWOPfZYnn/+eV566aWsS8mVnTt3DilYjxgxgmOPPfagth0OZdj8UIQQ3gq0ADcDbwbuBU6MMfbtdZ9PAp8EaGpqmnn77benUls12LZtG6NHj866DA3CPpW/vPRo3IOfBaD7jGszruTgzXjiagCWnfKFQ95GXvp0IHnu4VBVQp+qgX3KB/uUD/YpH4bbpzPPPHNpjHHWvm5LcwT8WeC5WEj8z4YQNgLHAGt23yHG+DXgawCzZs2KbW1tKZZX2drb2/H5LH/2qfzlpUfLHyn8ec9Dra+z6khgeLXnpU8HkuseDlEl9Kka2Kd8sE/5YJ/yIck+pRnALwHeCvxeCOGNwFhgXYqPL0nKgeXrNgMwPeM6JEmSSi3NAP514NYQwkNABC7Ze/q5JEmSJEmVLLUAHmPcBXwsrceTJEmSJKmc1GRdgCSp9EYObGd8/wZY05l1KZIkSSoygEtSpVnTSXPfKib0r4eF8wzhkiRJZcIALkmVpmsxgUgA6N8FXYuzrkiSJEmkuwibJOXbgrnM2LQJ2h7OupIDa24txu9IqG2A5tasK5IkSRKOgEtS5Zk0m66649lQ2wQX3Q2TZpf+MRbMLZwkSZI0ZI6AS1IF2lEzih2MoimJ8C1JkqRD4gi4JKmsuIK7JEmqVAZwSVL5cAX3PfwiQpKkymMAlySVD1dwL/CLCEmSKpIBXJJUPooruEeAal7B3S8iJEmqSAZwSVL5SGMF9zzwiwhJkiqSAVySVFZ21Izi5doJ1Ru+wS8iJEmqUB6GTJKkMuSh5CRJqjyOgEuSJEmSlAIDuCQN0fJ1m1m9tT/rMireir6J3NEzh6Wru7MuRZIkqaQM4JKksrF0dTdXvXIBt/WcwYU3dxjCJUlSRTGAS5LKRsfKjfRRywA19PYN0LFyY9YlSZIklYwBXJJUNlqmNFJHPzX0U19XQ8uUxqxLkiRJKhlXQZekCrSibyJP9h/HvNXdzJw8ruTbX75uMwDTS7zdmZPHcc3h3yrUfslVidQuSZKUFUfAJWmIRg5sZ0J8GdZ0Zl3KAeV9P+ppdWs597BHDd+SJKniGMAlaSjWdNLct4qj40uwcF5Zh3D3o5YkSSpPBnBJGoquxQQiAaB/F3Qtzrqi/XI/6srg4dgkSao8BnBJGormViKBCFDbAM2tWVe0X7v3o/74YYv55qUtTuXOobzvRiBJkvbNAC5JQzFpNl11x/NieANcdDdMmp11RQfkftT55m4EkiRVJgO4JA3RjppRbAjjyz58K//cjUCSpMrkYcgkSdVhwdzCv/PvybaOIfBwbJIkVSYDuCRJZWha3Vqm1a1luuFbkqSKYQCXJJWVzzdeB8CijOuQJEkqNfcBlyRJkiQpBQZwSZIkSZJSYACXpCFa0TeRb/e+y2MyAyMHtjO+fwOs6cy6FEmSpNwwgEvSECxd3c1Vr1zAN3vbuPDmjuoO4Ws6ae5bxYT+9bBwniFckiRpiAzgkjQEHSs30kctA9TQ2zdAx8qNWZeUna7FBCIBoH8XdC3OuiJJkqRccBV0SRqClimN1NFPH5H6unpapjRmXVJ2mluL8TsSahuguTXrioZk+brNAEzPuA5JklS9DOCSNAQzJ4/jmsO/xU96j+U3Lv0LZlbzsZknzaar7nhGxe00XfQNmDQ764okSZJywQAuSUM0rW4tb2Y1by9l+F4wt/Dv/HtKt02SP5b2jppR7GAUTQmE70WXzyn5NiVJksqBAVySpDKU9JcokiQpfS7CJkmSJElSCgzgkqSq4LHLJUlS1pyCLklD9PnG69i0aRM/yLoQHbziscsDsXDs8ovudvE4SZKUOkfAJUmVz2OXS5KkMmAAlyRVvuKxyyNAjo5dLkmSKosBXJJU+YrHLt9Q2+T0c0mSlBn3AZckVYUkj10uSZI0FI6AS5IkSZKUAgO4JOmgreibyB09c1i6ujvrUiRJknLDAC5JOihLV3dz1SsXcFvPGVx4c0duQrhfGkiSpKwZwCVJB6Vj5Ub6qGWAGnr7BuhYuTHrkgaV1y8NJElSZTGAS1KGlq/bzPJ1m7Mu46C0TGmkjn5q6Ke+roaWKY1ZlzSoPH5pkJgFcwsnSZKUOldBlyQdlJmTx3HN4d/iyf7jmHfJVcycPC7rkga1+0uDPiL1dfW5+NJAkiRVHgO4JA3Rosvn0N7ennUZZWFa3Vqm1a1leg7CN+TzSwNJklR5DOCSpKqQty8NFl0+J+sSJElSibkPuCRJkiRJKXAEXJIqkKOnkiRJ5ccALkmqCp9vvA6ARRnXIUmSqpdT0CVJGg4P6yVJkobIAC5JkiRJUgoM4JKUoZED2xnfvwHWdGZdiiRJkhJmAJekrKzppLlvFRP618PCeYZwSZKkCmcAl6SsdC0mEAkA/buga3HWFUmSJClBBnBJykpzK5FABKhtgObWrCuSJElSgjwMmSRlZdJsuuqOZ1TcTtNF34BJs7OuaMg8pJckSdLBM4BLUoZ21IxiB6NoylH4zqtFl89JZLvL120GYHoiWy+9vNUrSVIlcQq6JEmSJEkpMIBLkiRJkpQCA7gkSZIkSSkwgEuSJEmSlAIDuCRJkiRJKTCAS5IkSZKUAgO4JEmqbgvmFk6SJCXMAC5JGVrRN5E7euawdHV31qVIkiQpYQZwScrI0tXdXPXKBdzWcwYX3txhCJckSapwBnBJykjHyo30UcsANfT2DdCxcmPWJekQjBzYzvj+DbCmM+tSVE6c1i5J2gcDuCRlpGVKI3X0U0M/9XU1tExpzLokHaw1nTT3rWJC/3pYOM8QLkmSDsgALkkZmTl5HNcc/i0+fthivnlpCzMnj8u6JB2srsUEIgGgfxd0Lc66IkmSVMbqsi5AkqrZtLq1TKtby3TDdz41txbjdyTUNkBza9YVDWrkwHZGxe2F0fpJs7MuR5KkqmIAlyQdtEWXz8m6hPIwaTZddcczKm6n6aJvlH+gLU6ZD8TClPmL7i7/miVJqiBOQZckaRh21Izi5doJ+QiyTpnfp+XrNrN83easy5AkVQEDuCRJ1aI4ZT4C5GTKvCRJlcQALknSMKzom8gdPXPycRz34pT5DbVNTj+XJCkDBnBJkg7R0tXdXPXKBdzWcwYX3tyRixCeqynzkiRVGAO4JEmHqGPlRvqoZYAaevsG6Fi5MeuSdAhGDmxnfP8Gj+MuSUqcAVySpEPUMqWROvqpoZ/6uhpapjRmXZIOVnFl+An96wsrwxvCJUkJ8jBkkpShzzdeB8CijOvQoZk5eRzXHP4tnuw/jnmXXMVMj+eeP/taGb4E0/N3r6o+fdhbkiRVEgO4JEnDMK1uLdPq1jLd8J1PxZXhIRJcGV6SlDCnoEuSpOrlyvCSpBSlGsBDCBNCCGtCCFPTfFxJklQBFswtnErMleElSWlJLYCHEOqBG4EdaT2mJEmSJEnlIs0R8C8BNwAvpPiYkiRJkiSVhVQWYQshXAy8FGP8QQjhygPc75PAJwGamppob29Po7yqsG3bNp/PHLBP5a/UPdq0qTApyL6XVpqvpXF9fUB+eri+p4mnBpqZfNd/c8K42kxrOdg+zdi0CYBlJX6uk3hOJvZuYTSv8OPv3cCWI/K9553vTflgn/LBPuVDkn0KMcZENvyqBwnhQSAWTzOAZ4F5McYX9/czs2bNikuWLEm8tmrR3t5OW1tb1mVoEPap/JW6R+fd+CgAiy6fU7JtKt3X0vJrTgdg+lUPpfJ4w7F0dTcXfPUB+qilob6Ob17akumh0w66T7v3/55/T8lqSOQ5WdPJwNfPLhzerG5k7hd3870pH+xTPtinfBhun0IIS2OMs/Z1WypT0GOMZ8QY3x1jbAOWAZ84UPiWJEml17FyI33UMkANvX0DdKzcmHVJmUvkOdnXscUlScLjgEuSNCyfb7wOgEUZ1zEULVMaqaOfPiL1dfW0TGnMuqTMJfKceGxxSdJ+pB7Ai6PgkiQpZTMnj+Oaw7/Fk/3HMe+SqzKdfn4olq/bDMD0Em4zkeekeGzxUXE7TRd9I9fTzyVJpeUIuCRJVWRa3Vqm1a1les7Cd5KSeE521IxiB6NoMnxLkvaS5mHIJEmSJEmqWo6AS1KGXP1ckiSpehjAJUlSVcvTQnqSpHxzCrokSZIkSSlwBFySpGFwNwLty4q+iYWV1Vd35261eUlSchwBlyRJuTByYDvj+zfAms6sSzmgpau7ueqVC7it5wwuvLmDpau7sy5JklQmDOCSJKn8remkuW8VE/rXw8J5ZR3CO1ZupI9aBqiht2+AjpUbsy5JklQmDOCSJKn8dS0mEAkA/buga3HWFe1Xy5RG6uinhn7q62pomdKYdUmSpDLhPuCSJKn8NbcW43ck1DZAc2vWFe3XzMnjuObwbxX2Ab/kKvcBlyTtYQCXJEnlb9JsuuqOZ1TcTtNF34BJs7Ou6ICm1a1lWt1aphu+JUl7MYBLklRF8nzM6x01o9jBKJpKHL5dyV6SlBb3AZckSZIkKQUGcEmSlAsr+iZyR88cD+slScotA7gkSSp7HltbklQJ3AdckiSVvX0dW7ucVxfP8772kqTkOAIuSZLKnsfWliRVAkfAJUlS2fPY2pKkSmAAlyRJueCxtSVJeecUdEmSJEmSUmAAlyRJkiQpBQZwSZIkSZJSYACXJEmSJCkFLsImSZJywWNrS5LyzhFwSZJUOgvmFk6SJOl1DOCSJEmSJKXAKeiSJFWRRZfPybqEquDzLEnaF0fAJUmSJElKgQFckiRJkqQUGMAlSZLywkXuJCnX3AdckiTlgvtVS5LyzhFwSZIkSZJS4Ai4JEkqmeXrNgMwPeM6JEkqR46AS5IkSZKUAgO4JEmSJEkpMIBLkiRJkpQCA7gkSZIkSSkwgEuSJEmSlAIDuCRJKpmRA9sZ378B1nRmXYokSWXHAC5JkkpjTSfNfauY0L8eFs4zhCt5C+YWTpKUEwZwSZJUGl2LCUQCQP8u6FqcdUUVZ/m6zXuOtS5Jyh8DuCRJKo3mViKBCFDbAM2tWVckSVJZMYBLkqTSmDSbrrrj2VDbBBfdDZNmZ12RJEllpS7rAiRJUuXYUTOKHYyiyfAtSdLrOAIuSZIkSVIKDOCSJEmSJKXAAC5JkkpmRd9E7uiZw9LV3VmXIklS2TGAS5Kkkli6upurXrmA23rO4MKbOwzhkiS9hgFckiSVRMfKjfRRywA19PYN0LFyY9Yl6WAsmFs4SZISYwCXJEkl0TKlkTr6qaGf+roaWqY0Zl1SxRk5sJ3x/RtgTWfWpUiSDoEBXJIklcTMyeO45vBv8fHDFvPNS1uYOXlc1iVVljWdNPetYkL/elg4zxAuSTnkccAlSVLJTKtby7S6tUw3fJde12ICkQDQvwu6FkOVH299+brNAEzPuA5JGipHwCVJkvKguZVIIALUNkBza9YVZc4p+ZLyxgAuSZKUB5Nm01V3PBtqm+Ciu6t+9Nsp+ZLyyAAuSZKUEztqRvFy7QTDN+x7Sr4klTkDuCRJkvLHKfmScshF2CRJkpS/Bc2KU/JHxe00XfQNZwVIygUDuCRJKpnPN14HwKKM61B12FEzih2MosnwLSknnIIuSZIkvdaCuYWTJJWQAVySJEmSpBQYwCVJkiRJSoEBXJIkKSdW9E3kjp45LF3dnXUpkqRDYACXJEnKgaWru7nqlQu4recMLry5o+QhfOTAdsb3b4A1nSXdriTp1wzgkiRJOdCxciN91DJADb19A3Ss3Fi6ja/ppLlvFRP618PCeYZwSUqIAVySJCkHWqY0Ukc/NfRTX1dDy5TG0m28azGBSADo3wVdi0u3bUnSHh4HXJIklcyiy+dkXULFmjl5HNcc/i2e7D+OeZdcxczJ40q38ebWYvyOhNoGaG4t3bYTtKJvYuH5WN1d2udDkhLiCLgkSVJOTKtby7mHPVr6sDlpNl11x7OhtgkuuhsmzS7t9hOQ9D7xkpQEA7gkSZLYUTOKl2sn5CJ8Q8L7xEtSQgzgkiRJyp1E94mXpIS4D7gkSZJyJ9F94iUpIQZwSZIk5XJBszubPgPA3+SkXklyCrokSVKVc0EzSUqHAVySJKnKuaDZ6y1ft5nl6zZnXYakCmMAlyRJqnIuaCZJ6XAfcEmSpJz4fON1ACwq8XZd0Oz1Rg5sZ1TcDms6c3NoNknlzxFwSZIkMa1uLece9qjhG2BNJ819q5jQvx4WziuEcEkqAQO4JEmStLeuxQQiAaB/F3QtzroiSRXCAC5JkiTtrbmVSCAC1DZAc2vWFUmqEAZwSZIkaW+TZtNVdzwbapvgorvdB1xSybgImyRJkvQaO2pGsYNRNOUpfC+YW/h3/j3Z1iFpvwzgkiRJSmaFdQOhJL2KU9AlSZIkSUqBI+CSJEnKpUWXz8m6BEk6KI6AS5IkSZKUAgO4JEmSJEkpSC2AhxBqQwi3hBAeDiE8FEI4Oa3HliRJkiQpa2nuA/4hgBjju0IIbcAXgA+n+PiSJEm55j7PkpRvqQXwGON/hBC+X7w4GdiU1mNLkiRJkpS1EGNM9wFDWAj8JvDbMcb7X3PbJ4FPAjQ1Nc28/fbbU62tkm3bto3Ro0dnXYYGYZ/Knz3KB/uUD/YpH4bTp4kP/CGjeYV1M/6ELUdMLXFlyVr/P//MUwPNTH7HBzhhXG3W5Qxq27ZtnP7zLwKw7JQvZFyN9se/e/kw3D6deeaZS2OMs/Z1W+oBHCCEcDTwGHBSjHH7vu4za9asuGTJknQLq2Dt7e20tbVlXYYGYZ/Knz3KB/uUD/YpHw65T2s6Gfj62QQioW4kXHQ3TJpd8vqSsHR1Nxd89QH6qKWhvo5vXtrCzMnjsi7rgNrb2znuoT9iVNxO08XfyM1zXW38u5cPw+1TCGG/ATzNRdg+HkK4snjxFWCgeJIkSVKl6VpcCN8A/buga3HWFQ1Zx8qN9FHLADX09g3QsXJj1iUNauzmp2nuW8WE/vWwcB6s6cy6JEn7kOZhyL4LnBJCeBD4AfCZGOOOFB9fkiRJaWluJRKIALUN0NyadUVD1jKlkTr6qaGf+roaWqY0Zl3SoI7c9FRuv/CQqkmai7BtB85N6/EkSZKUoUmz6ao7vjAl+qJ8TYmeOXkc1xz+LZ7sP455l1xV9tPPATYdeXIxfkdCzr7wkKpJmochkyRJUhXZUTOKHYyiKUfhe7c7mz4DwN/kIHwDbDliam6/8JCqiQFckiRJqgB5/sJDqhZp7gMuSZIkSVLVMoBLkiQpESv6JnJHzxyWru7OuhRJKgsGcEmSJJXc0tXdXPXKBdzWcwYX3txhCJckDOCSJElKQB6PpS1JSTOAS5IkqeTyeCztvHPKv1T+DOCSJEkquZmTx3HCMUfxxnGj+ealLbk4lnaePdfd75R/KQcM4JIkSUrEmBH1TDxypOE7BU//qt8p/1IOGMAlSZKknJt6VK1T/qUcqMu6AEmSJEnDc8K4Wq45/Fs82X8c8y65ylkHUpkygEuSJEkV4M6mzwDwN4ZvqWw5BV2SJEmSpBQYwCVJkiRJSoFT0CVJkqTXWHT5nKxLkFSBHAGXJEmSJCkFjoBLkiQpEY4iS9KrOQIuSZIkSVIKhjQCHkJ4y2D3iTE+O/xyJEmSJEmqTEOdgv4Y8AQQ9nP724GjSlKRJEmSJEkVaKgB/Nsxxsv2d2MI4aYS1SNJkiTpELjPvVT+hrQP+IHC91BulyRJkiSp2g1rEbYQwrdLVYgkSZIkSZVsuKugO/ItSZIkSdIQDCuAxxi7S1WIJEmSJEmVbKiLsAEQQlgFxL2u2hJjnFHSiiRJkiRJqkAHFcCBqcV/AzAT+Ghpy5EkSZIkqTId1BT0GGNP8bQzxvgw8I6E6pIkSZIkqaIc7BT0L/LrKejHAAMlr0iSJEmSpAp0sFPQn97r/E+A+0pYiyRJkiRJFeugAniMcWFShUiSJEmSVMmGdRiyEMK3S1WIJEmSJEmV7JACeAihsXj2shLWIkmSJElSxTqoAB5C+EAIYSXwXyGEZ4G3J1OWJEmSJEmV5WAXYftr4LQY40shhKOB/wBaSl6VJEmSJEkV5mCnoG+NMb4EEGN8Edhe+pIkSZIkSao8QxoBDyFcs/v+IYTvAw8Bs4GepAqTJEmSJKmSDHUK+jPFf58D+ovnv1f6ciRJkiRJqkxDCuC7j/8dQngwxnhGsiVJqhoL5hb+nX9PtnVIkiRJKTjYRdhiCOEuCiPiAwAxxqtKXpWk8mJQliRJkobtYAP4LYlUIUmSJElShTuoAL57KvpuIYT60pYjSZIkSVJlOqjDkIUQLg8hPBtCWBlCWAUsT6guSZIkSZVuwdxf7+omVYGDPQ747wPvBu4F5gM/K3lFkiRJkiRVoIMN4C/EGNcBY2KM7cARpS9JkiRJkqTKc7ABfHMI4TcorIZ+OTC+9CVJkiRJklR5DjaAXwqsBq4E3gJ8uuQVSZIkSZJUgYYUwEMInwGIMW6NMT4RY3whxvjHxWnoe26XJEmSJEn7NtTDkP1RCGHCfm4LwAXAP5akIkmSJEmSKtBQA/hfDXL7Xw+3EEnVZ/m6zQBMz7gOSZIkKQ1DCuAxxoVJFyJJkiRJUiU72EXYJEmSJEnSIRh0BDyE8FXgp8CTwE9jjFsSr0pSWUlqqvjIge2MitthTSdMml3irUuSJEnlZSgj4E8AJwFfAFaFEFaHEP4zhHBNCOH8ZMuTVLHWdNLct4oJ/eth4bxCCJckSZIq2KAj4DHGr+19OYRwLPBW4G3AXOD2ZEqTVNG6FhOIBID+XdC12FFwSZIkVbShroK+R4zxeeB54N7SlyOpajS3FuN3JNQ2QHNr1hVJkiRJiXIRNknZmDSbrrrj2VDbBBfd7ei3JEnVqGcLbF7jrmiqGgZwSYMaObCd8f0bSv7muKNmFC/XTjB8S5JUjdZ0wvqnYNNq14NR1TCASzowF0uTJEkL5hZOpdS1GOJA4fzu9WCkCmcAl3Rg+1osTZIkHZokgmxeNbdCKMYR14NRlTCASzqw4mJpEXxzlCRJpTNpNitrm1nvejCqIgZwSQfmYmmSJCkhrgejanPQhyGTVH121IxiB6No8s1RkiRJOmSOgEvKzIq+idzRM4elq7uzLkWSJElKnAFcUiaWru7mqlcu4LaeM7jw5g5DuCRJkiqeAVxSJjpWbqSPWgaoobdvgI6VG7MuSZIkSUqUAVxSJlqmNFJHPzX0U19XQ8uUxqxLkiRJkhJlAJcqRc6OKzpz8jiuOfxbfPywxXzz0hZmTh6XdUmSJCWvZwtsXgNrOrOuRFIGDOCSMjOtbi3nHvao4VuSVB3WdML6p2DTalg4L18h3C8OpJIwgEvKzOcbr+PzjddlXYYkSenoWgxxoHC+f1fhch7k+YsDqcwYwCVJkqQ0NLcyQCAC1DZAc2vWFQ1NXr84kMqQAVzSoDxetyRJJTBpNl11x7Ohtgkuuhsmzc66oqFpboVQjA15+uJAKkMGcEkH5PG6JUkqnR01o3i5dkJ+wjcUam06GY6cnK8vDqQyZACXdEAer1uSJHHYWDhikuFbGqa6rAuQVN52H6+7j0h9XX0+jte9+3Bs8+/Jtg5JkirE8nWbAZhe4u3uXox1UYm3K5UrR8AlHZDH65YkSUnZurOXtZt2uIubqoYBXNKgPF63JEkqtaWru3n6xa08373DdWZUNQzgkiRJklLXsXIjA7Fw3nVmVC3cB1ySJElKyZ+OuoYtO/u4fnV31c8sa5nSSE2AgQj1dTX5WGdGGiZHwCVJkqQUOOX61WZOHsfUo8dw7LiRrjOjquEIuKRBuUKpJEnDt68p19UeOseMqGfMiPqqfx5UPQzgkjKz6PI5WZcgSVJq8jzl2i/jpdIwgEuSJEkp2D3lesvOPq4//xRHfaUq5D7gkiRJUkrGjKhn4pEjcxe+PV63VBoGcEmSJEn75eJxUukYwCVVnJVrX2D98z+HNZ1ZlyJJUu55vG6pdFIJ4CGE+hDCbSGExSGEzhDCvDQeV1IVWtNJc98qJvSvh4XzDOGSJA3T7sXjIH+Lx0nlJq0R8P8FbIwxtgLnAP83pceVVG26FhOIBID+XdC1OOuKJEnKNY/XLZVOWqug3wl8u3g+AH0pPa6katPcWozfkVDbAM2tWVckSVLuebxuqTRCjDG9BwthDHA3cFOM8d/3cfsngU8CNDU1zbz99ttTq63Sbdu2jdGjR2ddhgYxnD7NeOJqAJad8oVSlgTAFx/bAcCVp40s+baTMPGBP2Q0r7Buxp+w5YipJd22r6V8sE/5YJ/ywT6VVlLvqUn3Kam68/YZY7h8PeXDcPt05plnLo0xztrXbakdBzyEMAm4C/jXfYVvgBjj14CvAcyaNSu2tbWlVV7Fa29vx+ez/A2rT6uOBEikz1995tHitueUfNtJWP7IWH7FWN7x4d8t+bZ9LeWDfcoH+5QP9qm0knoqk+5TUp8F8vYZY7h8PeVDkn1KJYCHEJqA+4E/iDH+dxqPKUmSJElSOUlrEbargHHAX4YQ2oun6phnIkmSJEkSKY2Axxj/N/C/03gsSZIkSZLKUWr7gEtK1vJ1mwGYnsC2F11eHftlSZKkdPkZQ9UmrSnokhI2cmA74/s3wJrOrEuRJEmStA8GcKkSrOmkuW8VE/rXw8J5VR/CV/RN5I6eOSxd3Z11KZIkSdIeTkGXKkHXYgKRANC/C7oWw6TZWVeViaWru7nqlQvoo5ZFN3fwzUtbmDl5XNZlSZKUa04Vl0rDEXCpEjS3EglEgNoGaG7NuqLMdKzcSB+1DFBDb98AHSs3Zl2SJEmSBBjApcowaTZddcezobYJLrq7ake/AVqmNFJHPzX0U19XQ8uUxqxLkiRJkgCnoEsVY0fNKHYwiqYqDt8AMyeP44RjjmLLzj6uP/8Up59LkiSpbBjAJVWcMSPqGTOi3vAtSZKksuIUdEmSJEmVY8Hcwikv21VVMYBLkiRJkpQCA7gkSZIkSSkwgEuSJEmSlAIDuCRJkiRJKTCAS5IkSZKUAgO4JEmSJEkpMIBLkiRJqhw9W2DzGljTmY/tqqoYwCVJkiRVhjWdsP4p2LQaFs4rXVhOartp8PjlZaUu6wIkqdQWXT4n6xIkSVIWuhZDHCic799VuDxpdvluV1XHEXBJkiRJlaG5FUIx4tQ2FC6X83ZVdQzgkiRJkirDpNmsrG1mfW0TXHR36Uapk9quqo5T0CVJkiRVjB01o9jBKJpKHJKT2q6qiyPgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZJUqXq2wOY1sKYz60qEq6BLkiRJqiB/Ouoatuzs4/rV3cycPK5k2/1843UALCrZFlOwphPWPwVxABbO8xBqZcARcEmSJEkVYenqbp5+cSvPd+/gwps7WLq6u2Tb3rqzl7WbdpR0m4nrWlwI3wD9uwqXlSkDuCRJkqSK0LFyIwOxcL63b4COlRtLst0kg32imlshFCNfbUPhsjJlAJckSZJUEVqmNFITCufr62pomdJYku0mFewTN2k2NJ0MR07O9/TzBXMLpwpgAJcqxIq+idzRMyc/38hKkiSV2MzJ45h69BiOHTeSb17aUrJ9wJMK9qk4bCwcMSm/4bvCGMClCrB0dTdXvXIBt/Wcka9pUZIkSSU2ZkQ9E48cWdIF2JIK9qo+BnCpAnSs3EgftQxQk69pUZIkSTmRRLBX9TGASxWgZUrjngU2cjctSpIkSaoSHgdcqgC7p0Vt2dnH9eef4jezkiRJUhkygEsVYsyIesaMqDd8S5IkSWXKKeiSJEmSpOGpoEOFJckRcEmSJEkVY9Hlc7Iu4eDsDq3z78m2DqXCAC5JkiRJg8hdsN/NYF9WnIIuSZIkSVIKDOCSJEmSpPLVswU2r4E1nVlXMmwGcEmSJEnS8CQVktd0wvqnYNNqWDgv9yHcAC5JkiRJOnRJhuSuxRAHCuf7dxUu55gBXJIkSZJ06JIMyc2tEIqxtbahcDnHXAVdkiRJknTodofkOFD6kDxpNjSdDDs3w2/dXLicYwZwKQse71GSJEmVIumQfNjYwinn4Rucgi5JkiRJ2amUFb4PGwtHTKqIkJwkA7gkSZIkZaHCVvjW4AzgkiRJkpSFClvhW4MzgEuSJElSFipshW8NzkXYJEmSJCkLlbTCt4sLD4kBXJIkSZKyUkErfGtwTkGXJEmSJCkFBnBJkiRJklLgFHRJkiRJUvmqoP3LHQGXJEmSJCkFBnBJkiRJklJgAJckSZIkKQXuAy5JkiRJWamg/Zs1OEfAJUmSJElKgQFcykLPFti8BtZ0Zl2JJEmSKth5Nz7KeTc+mnUZKjKAS2lb0wnrn4JNq2HhPEO4JEmSVCUM4FLauhZDHCic799VuCxJkiSp4hnApbQ1t0IovvRqGwqXJUmSJFU8A7iUtkmzWVnbzPraJrjobpg0O+uKJEmSpGFxX/Oh8TBkUgZ21IxiB6NoKmH4XnT5nJJtS5IkSVLpOQIuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZKkYdm6s5e1m3awdHV31qWUNQO4JEmSJOmQLV3dzdMvbuX57h1ceHOHIfwADOCSJEmSVKHSGJnuWLmRgVg439s3QMfKjYk9Vt4ZwCVJkiSpAqU1Mt0ypZGaUDhfX1dDy5TGRB6nEhjAJUmSJKkCpTUyPXPyOKYePYZjx43km5e2MHPyuEQepxLUZV2AJEmSJKn0do9MD8TkR6bHjKhnzIh6w/cgDOCSJEmSVIF2j0xv2dnH9eefYjguAwZwSZIkScrIeTc+CsCiy+cksn1HpsuL+4BLkiRJkpQCA7gkSZIkSSlwCrqUgc83XgfAoozrkCRJkkohqSn0lcYRcEmSJEmSUmAAlzKwdWcvazftYOnq7qxLkSRJkpQSA7iUsqWru3n6xa08372DC2/uMIRLkiRJVcIALqWsY+VGBmLhfG/fAB0rN2ZbkCRJkqRUGMCllLVMaaQmFM7X19XQMqUx24IkSZIkpSLVVdBDCKcBfx9jbEvzcaVyMnPyOKYePYYtO/u4/vxTmDl5XNYlSZIkqUK5Onl5SW0EPITwZ8DNwIi0HlMqV2NG1DPxyJGGb0mSpCrn4rzVJc0p6L8APpLi40mSJElS2XJx3uoTYozpPVgIzcDtMcaW/dz+SeCTAE1NTTNvv/321GqrdNu2bWP06NFZl6GiLz62A4ArTxv5quvtU/mzR/lgn/LBPuWDfcoH+5QPr+3T93+xi2//vBcojIx+5M31fPBNDRlVp92G+3o688wzl8YYZ+3rtlT3AR9MjPFrwNcAZs2aFdva2rItqIK0t7fj81k+vvrMowC0tb16nxz7VP7sUT7Yp3ywT/lgn/LBPuXDa/s05vhuvvvcIwxEaKiv4YL3nOouivtw3o2Fz85p7c+e5OuprAK4JEmSJFULF+etPgZwSZIkScrImBH1jBlRb/iuEqkG8BhjF7DP/b8lSZIkSapkaa6CLkmSJElS1TKAS5IkSZKUAgO4JEmSJEkpMIBLkiRJkpQCA7gkSZIkSSkwgEuSJEmSlAKPAy5JkiRJGVl0+ZysS1CKHAGXJEmSJCkFjoBLGfCbTkmSJGlotu7sZcvOPpau7mbm5HFZlzMsjoBLkiRJksrS0tXdPP3iVp7v3sGFN3ewdHV31iUNiwFckiRJklSWOlZuZCAWzvf2DdCxcmO2BQ2TAVySJEmSVJZapjRSEwrn6+tqaJnSmG1Bw+Q+4JIkSZKksjRz8jimHj2GLTv7uP78U3K/D7gBXJIkSZJUtsaMqGfMiPrch29wCrokSZIkSakwgEuSJEmSlAIDuCRJkiRJKTCAS5IkSZKUAgO4JEmSJEkpcBV0SZIkSVLZ6mr4UvHcdzKtoxQcAZckSZIkKQUGcEmSJEmSUmAAlyRJkiQpBQZwSZIkSZJSYACXJEmSJCkFBnBJkiRJUtk6rrGGcWO3s2zDsqxLGTYDuCRJkiSpLC3bsIxnup9h7ba1XHb/ZbkP4QZwSZIkSVJZWrJ+CZEIQO9AL0vWL8m4ouExgEuSJEmSytKsplkEAgD1NfXMapqVcUXDU5d1AZIkSZIk7cuMCTM4cdyJbO3dyrWt1zJjwoysSxoWA7gkSZIkqWyNbhjN6IbRuQ/f4BR0SZIkSZJSYQCXJEmSJCkFBnBJkiRJklJgAJckSZIkKQUuwiZJkiRJKlsLzlmQdQkl4wi4JEmSJEkpMIAP14K5hZMkSZIklZn5981n/n3zsy5DRQZwSZIkSZJSYACXJEmSJCkFBnBJkiRJklJgAJckSZKkCrVt1zbWbV/Hsg3Lsi5FGMAlSZIkqSIt27CMZ7qfYe22tVx2/2WG8DJgAJckSZKkjCS5SvmS9UuIRAB6B3pZsn5JIo+joTOAS5IkSVIFmtU0i0AAoL6mnllNszKuSHVZFyBJkiRJKr0ZE2Zw4rgT2dq7lWtbr2XGhBlZl1T1DOCSJEmSVKFGN4xmdMNow3eZcAq6dCAL5hZOkiRJUgJcpby6GMCHaX5Yz/ywPusypEQX8JAkSVLpuUp59XEKuiRJkiRlYF+rlJd6qviCcxaUdHsaHkfAJUmSJCkDrlJefRwBlyRJkqQMuEp59XEEfJjetHMHczf9CtZ0Zl2KktCzBTavsb+SJElKxOiG0Rwz6hjDd5UwgA/Hmk7+dP1afnPTRlg4z5BWadZ0wvqnYNNq+ytJkiRp2JyCPhxdi6mLkVqA/l3QtRgmzc66KpVK12KIA4Xz9leSJEkJcJG06uII+HA0t9IXAv0AtQ3Q3Jp1RSql5lYIxZeI/ZUkSZI0TAbw4Zg0m+uaJnLXkY1w0d2OjlaaSbOh6WQ4crL9lSRJkjRsBvBh+mlNPXccPtZwVqkOGwtHTLK/kiRJ0gHMv28+8++bn3UZZc8ALlWIbbu2sW77OpZtWJZ1KZIkSaoyfhYdGgO4VAGWbVjGM93PsHbbWi67/zL/8EmSJCk1fhYdOgO4lIFST9FZsn4JkQhA70AvS9YvKdm2JUmSpAPxs+jQeRiyYXolRF6piSzbsIwZE2ZkXU51WzC38O/8e0q3zVJuK0GzmmYRCEQi9TX1zGqalXVJkiRJqhJ+Fh06R8CHYdmGZfyyfoCXaqNTLSrUeTc+ynk3Ppp1GYOaMWEGJ447kYmjJ3LT2Tf5ZZAkSZJS42fRoXMEfBgKUy2A8OupFv6yZahnC+zcDGs6q3LV8tENoxndMNrfQUmSJKXuznl3Zl1CLjgCPgyFqRZAxKkWWVvTCeufgk2rYeG8wmWpinkoEEmSpPJjAB+GGRNmcFxvDW/oD061yFrXYogDhfP9uwqXJUmSVNb8wljVxgA+TIfHwPj+mkTD9/xbZzH/VkfXD6i5FULx17m2oXC5jHmcREmSJKn6GMBVGSbNhqaT4cjJcNHdZb0PuMdJlCRJkqqTAVyV47CxcMSkkobvZ/sXsnzr/Sxd3V2ybXqcREmSpAJnBaraGMCVrgVzf3287jK3dHU33Wvez7aX38GFN3eULITvPk4iuHifkpP0Bxr32UteUs+xvZNULpwVqGpkAM+BI3btYOzOrSx76t+TfaAcheN9mn9P4VQiHSs3UjPieRoaH6C/bhUdKzeWZLt5PE6iH9jzxQ80kqRSS+KzgLMCVY08Dvgwra5/U6LbX/bUv/NQPfSGeh55/BpuAmac/LFEHmt+WA/AgkS2XpSjY3WPb1zH4cd9HUIfxDrGN74VOKEk2/aY3UrSvj7Q+LsmSQdnd9hccE6in4xyY9uubWzt3cqyDctK9p6ye1ZgJDorUFXDEfAyt2TlD+gNgYEQ6A2Fy7mV8LG6z7vxUc678dGSbW9reIYQegkhUlvTz9bwTMm2nYQF5yzwQ0IOJTGikMZuDu6zl7yknmN7J+lgJTWzKo+zAqXhMoCXuVlT3kd9jNTGSH0sXE5K4lPdEz5Wd1fDl+hq+FLJtlcILQEiNNQ2VPW3sn5gz5ekP9A4xT15ST3H9i7/3CXo1ZJ8Pnzv+7Ukp4qPbhjNMaOOMXyrahjAy9yMkz/G6b3w7p293HTqVYlNP9891b19RD2XPX5NMiG8uZWeEOiHRI7V3c8OevlVSb+VPSweS30cX9Xfyib5gd0Pksl9wEvyA4377CUvqefY3qnSJPU31C+rXs0FZKXSMYDnwOaGkWwZMSax8A0pTXWfNJvrmiZy15GNJT9W97INy+gJz9MbXi7pG2UtI6nnqJKHmDxNF0/yA3tSH5ySXD36+hevL9n28voBzw9iyUvqObZ36Unq75Cjsr+W5N/QJN/78vjlc5Izq5L4TPTRuz/KOd85p+Svk6S2q+piABeQ3lT3n4w4jJuPHMuywxpKut3CG2OEALv6dzmqU0JJfWBP8oNTkvvO/qqvdLMs8joa6T57yUvqObZ36Uni71Bev7RLSpJ/Q5P8siqvX6LcOe9O7vut+8r+74a78KjcGcAFpDPVfdmGZTwT+lgb+kv+h2tMPJEY64kx0D9Qy5h4Ykm227zrT2je9Scl2VZeJfWBPakPTkm/8f6q/1cl226SH/CSnmXhPnvJS+o5tnfJS+rvUF6/tEtKkn9Dk3rvSzrEOULrLjwqfwbwYWrc8r+p3/D7LF3dnXUpw5b0VPck/3C9vPEYXvnl77DrpbPp+eWlvLzxmJJsd9Hlc1h0+ZySbCvPkvjAntQHpzy98ToauW9JTc/0g2k6fJ6T+zuU9C4ESbz2lm1Yxs1P3pzI70PSf0OTGPFN8rPQqp5VjtDiLjwqfx4HfBiWru7m6Re3MhDhwps7+OalLcycPK7kj7Pg4pS+YTv6rYluflbTLIgBiNTXlvYPV8uURri/mV07mhlRX1O4rLK2+4PT1t6tXNt6bdkfUzSp7eb1mPBJjq4ncazZ3aNOkchl919W0g/rSR0rOC/rROwtyec5SR+9+6Ml/VuU1N+LpP5u7lbq196yDcv4xL2fIBIZUTsisZCcJ0ke9/rnO3/+unCfh9dfqSX1Okn69afqYQAfho6VGxko/J2jt2+AjpUbEwngu49tnfRIbNIf9navKj7ADm46+x9K+odr5uRxTD16DFt29nH9+ack0odqltTvRhLhM+k33g1bNnD92deXbLt5DFlJSirA7WvUqVQ9TOILgyQl+TuX5PMMpQ/KkMzvXJIf1JP60i6J5yHp34c8mjFhBv/2/n9jyfolzGqaVdLn480j3pxYuM+bpL6YydsXPipPBvBhaJnSSE2AgQj1dY66DsWbdv0lQCJvwGNG1DNmRL3hO0eSCgJJvvG2t7dX/QfIJCX1gT2pUae8jvgmJcnRvbx9OZPU36Gk/m4m8Twk+fuQZzMmzEjk78Txhx2fWLiXVDoG8GGotFHXNEbat+7sZcvOPpau7s798yWp9JKcupvEB1NH+F4tyZHfvH05kzdJPA9JjvZq35IK95JKxwA+TI66Dl1a+8xLyq8kP7An8cHU8PZ6SY385u3LmbxJ6nkwEErSqxnAlZqk95l3tXKpMuTpA7vhLT15+3Imj3weJCl5qQXwEEIN8K/A24Ee4NIY43NpPb6y5z7zkiqRoSU9PteSpLxL8zjgvwGMiDHOAT4LfDnFx9YQbN3Zy9pNOxI7pvnufeaPHTfS6eeSJEmSqk6aAfx04D6AGGMH4I5yQ5R0MIZf75/9fPcOLry5I7HHGjOinolHjjR8S5IkSao6aQbwscDmvS73hxDcB30QaQXjfe2fLUmSJEkqnRBjTOeBQvgK0BFjvKN4+fkY47Gvuc8ngU8CNDU1zbz99ttTqW04vvjYDgCuPG1kItv//i928e2f9wKFb0s+8uZ6PvimhoPezrZt2xg9evR+b3+uu58vPLaTCDTUwJ+dOoITxtUeYtX7l/TzlXeD9UnZs0f5YJ/ywT7lg33KB/uUD/YpH4bbpzPPPHNpjHGfM77THIF+GPgQcEcIoQV48rV3iDF+DfgawKxZs2JbW1uK5R2arz5TOHZ2W1syK3CPOb6b7z73CAMRGupruOA9px7S9O329nYO9Hy2Ad/95YOJH9M8By3N1GB9UvbsUT7Yp3ywT/lgn/LBPuWDfcqHJPuUZgC/C3hvCOERIADzU3zs3Nq9cFnSwRg8prkkSZIkJSm1AB5jHAB+N63HqyQGY0mSJEnKvzQXYZMkSZIkqWq5Crn2WHR5MvuxS5IkSZIcAZckSZIkKRUGcEmSJEmSUmAAlyRJkiQpBe4DngPumy1JkiRJ+ecIuCRJkiRJKTCAS5IkSZKUAgO4JEmSJEkpcB/wYXL/bEmSJEnSUDgCLkmSJElSCgzgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkmSJElSCgzgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkmSJElSCgzgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkmSJElSCgzgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkmSJElSCkKMMesa9imE8BKwOus6Ksh44OWsi9Cg7FP5s0f5YJ/ywT7lg33KB/uUD/YpH4bbp8kxxjfs64ayDeAqrRDCkhjjrKzr0IHZp/Jnj/LBPuWDfcoH+5QP9ikf7FM+JNknp6BLkiRJkpQCA7gkSZIkSSkwgFePr2VdgIbEPpU/e5QP9ikf7FM+2Kd8sE/5YJ/yIbE+uQ+4JEmSJEkpcARckiRJkqQUGMArRAjhtBBC+z6u/1AI4fEQwqMhhMtec9tvhhD+PbUiZZ9ywj7lg33KB/uUD/ap/NmjfLBP+ZBln+qGuwFlL4TwZ8DHge2vub4e+Afg1OJtD4cQ7o4xrg8hXA+8D1iWcrlVyz7lg33KB/uUD/YpH+xT+bNH+WCf8iHrPjkCXhl+AXxkH9dPA56LMXbHGHcBDwFnFG97BPhUSvWpwD7lg33KB/uUD/YpH+xT+bNH+WCf8iHTPhnAK0CM8TtA7z5uGgts3uvyVuCI4s8sAlyBL0X2KR/sUz7Yp3ywT/lgn8qfPcoH+5QPWffJAF7ZtgBj9ro8BtiUTSk6APuUD/YpH+xTPtinfLBP5c8e5YN9yodU+mQAr2wrgDeHEI4KITRQmELxaMY16fXsUz7Yp3ywT/lgn/LBPpU/e5QP9ikfUumTi7BVoBDCx4DRMcavhRCuAH5A4cuWW2KMa7OtTrvZp3ywT/lgn/LBPuWDfSp/9igf7FM+pN2nEKO7HEiSJEmSlDSnoEuSJEmSlAIDuCRJkiRJKTCA51wIoSaEcEMI4dEQQnsI4YTX3H5ZCGFJCKEjhPDB4nX/WLxvewjh6RBCRzbVVw/7lA/2KR/sUz7Yp3ywT/lgn/LBPuVD5n2KMXrK8YnCQeRvLZ5vAb63121HA08Ch1E4ht2TwGF73V4PPAa8Nev/R6Wf7FM+TvYpHyf7lI+TfcrHyT7l42Sf8nGyT/k4Zd0nR8Dz73TgPoAYYwcwa6/bZgMPxxh7YoybgeeAt+11+6eB+2OMT6ZVbBWzT/lgn/LBPuWDfcoH+5QP9ikf7FM+ZNonD0OWf2OBzXtd7g8h1MUY+/Zx21YK3+QQCse2u5zCL5mSZ5/ywT7lg33KB/uUD/YpH+xTPtinfMi0T46A598WYMxel2uKvzz7um0MsKl4/j3Ag8VvdpQ8+5QP9ikf7FM+2Kd8sE/5YJ/ywT7lQ6Z9MoDn38PABwBCCC0U9lPYrRNoDSGMCCEcAUwDnire9h7g3jQLrXL2KR/sUz7Yp3ywT/lgn/LBPuWDfcqHTPvkFPT8uwt4bwjhESAA80MIVwDPxRjvDiH8E7CYwpctV8cYdxZ/7kTg3zKpuDrZp3ywT/lgn/LBPuWDfcoH+5QP9ikfMu1TKK7mJkmSJEmSEuQUdEmSJEmSUmAAlyRJkiQpBQZwSZIkSZJSYADPuRBCTQjhhhDCoyGE9hDCCa+5/bIQwpIQQkcI4YPF68aHEO4PISwOISwKIRyeTfXVwz7lg33KB/uUD/YpH+xTPtinfLBP+ZB5n2KMnnJ8Aj4C3Fo83wJ8b6/bjqawrP5hFA4gv/v8PwEXF+/zWeCPsv5/VPrJPuXjZJ/ycbJP+TjZp3yc7FM+TvYpHyf7lI9T1n1yBDz/TgfuA4gxdgCz9rptNvBwjLEnFg4Y/xzwtr1/hsKx7N6TXrlVyz7lg33KB/uUD/YpH+xTPtinfLBP+ZBpnwzg+TcW2LzX5f4QQt1+bttK4Zucva/ffZ2SZZ/ywT7lg33KB/uUD/YpH+xTPtinfMi0Twbw/NsCjNnrck2MsW8/t40BNr3m+t3XKVn2KR/sUz7Yp3ywT/lgn/LBPuWDfcqHTPtkAM+/h4EPAIQQWijsp7BbJ9AaQhgRQjgCmAY8tffPAO8HFqdXbtWyT/lgn/LBPuWDfcoH+5QP9ikf7FM+ZNqnUNyRXDkVQqgB/pXCvgkBmE/hl+O5GOPdIYTLgE9S+LLlmhjjd0IITcBCCt/evAx8LMa4PZP/QJWwT/lgn/LBPuWDfcoH+5QP9ikf7FM+ZN0nA7gkSZIkSSlwCrokSZIkSSkwgEuSJEmSlAIDeIUIIZwWQmh/zXVHhxDa9zptCiH8bihYu9f1X8yo7KoRQqgPIdwWQlgcQugMIcx7ze0fCiE8HkJ4tLjfCSGEkSGE7xR/5v+FEN6QTfXVYwh9uiCE8FgI4eEQwg3FfYgIIfx4r9fTgmyqrx5D6NMfhRCW79WTE309pe9AffL9qXyEEGpDCLcU/649FEI4+TW3+/5UBobQJ9+fysAQ+uT7Uxk4UJ9SeX+KMXrK+Qn4Mwqr93Uc4D5zgB8BtcAJwH9mXXc1nSgs7vCPxfNHAb/c67Z64DlgHNAAPA40AVcAnyve53zg+qz/H5V+GqRPI4FfAIcXL38LmAeMAJ7IuvZqOh2oT8XrvgHMfM11vp7KrE973c/3p2z79BvALcXzbcD39rrN96cyOQ3SJ9+fyuR0oD4Vr/P9qQxOg/Vpr/sl8v7kCHhl+AXwkf3dGEIIwD8Dn4ox9gMzgYkhhP8pftN2Ykp1VrM7gb8sng9A3163TaOw6mJ3jHEX8BBwBnA6cF/xPvcC70mp1mp2oD71AO+MMb5SvFwH7ATeDhweQrg/hPCjUDichZJ1oD5B4W/clcVvta8sXufrKX2D9cn3pzIQY/wPCqv9Akzm1ce29f2pTAzSJ9+fysQgfQLfn8rCEPqU6PtT3aH+oMpHLCyN33yAu3wIWB5jfKZ4eR3wxRjjnSGE0yl8G3dqwmVWtRjjNoAQwhjg28Bf7HXzWGDzXpe3Ake85vrd1ylBB+pTjHEAWF+8/dPAaOCHwMnAl4CbgTcD94YQTowxvi5sqDQGeT0B3A78C7AFuCuE8EF8PaVuCH0C35/KQoyxL4SwEPhN4Lf3usn3pzKyvz75/lReDvB6At+fysYgfYIE358cAa8O/wv42l6XlwDfA4gxPgS8sfgtjxIUQpgE/A9wW4zx3/e6aQuFYwruNobCN3F7X7/7OiXsAH0ihFATQvgS8F7gt2JhftKzwDdiwbPARuCYtOuuNvvrU/Fv2T/GGF8ujtjdA5yCr6dMHOj1VOT7U5mIMV4EvAW4KYQwqni1709lZj998v2pzOyrT74/lZ/9vZ6KEnt/MoBXh1nAI3td/mvgMwAhhLcDa4p/qJWQEEITcD/w5zHGW15z8wrgzSGEo0IIDRSm9z0KPAx8oHif9wOL06q3Wg3SJ4AbKexT9xt7TfW7BPhy8effSOGb7HUplFu1BunTWOCpEMLo4hvjWcBSfD2lbgivJ/D9KXMhhI/vNRX2FWCgeALfn8rGIH0C35/KwiB98v2pTAzh9QQJvj8F39cqQ3EK+u0xxpYQwseA0THGrxVXUvxhjHHGXvcdR2HaxGgK++T9fozx6QzKrhohhOuB84C9n+ebgFHFPn0I+CsKX4rdEmP8lxDC4cBCCt9W7wI+FmN8MeXSq8qB+kThm88lFN4Yd//hvJ7CN9i3AscVr//zGOPef7BVYkN4PX0c+EMK+0X+d4zxr309pW8IffL9qQwUR30WAEdTWHTtWgp/80b7/lQ+DtQnfH8qG0N4Pfn+VAaG0KdE358M4JIkSZIkpcAp6JIkSZIkpcAALkmSJElSCgzgkiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkkqeyGE3wohPBZC+EkIYUkI4X1Z1zRUea09r3VLklTODOCSpLITQrg4hPDLEMIVIYSPAX8CfDjG+HbgAmBhCOHYg9zOhhBCewjhf0IIS0MId4YQGor3awsh3P6an93XddcWt3lzCGFTCGHqII9/SLW/pu62/dV+sHUXzw9a+8HWnWW9r3m83ac791ff/v7PgwkhPBBCOOs1110fQrh0rxr/ayi/F5Kk6mUAlySVq38HbgSuBc6NMb4IEGP8OdAO/H8HsZ0fAz+KMbbFGM+MMc4EeoF5h1JYjPFSYNmB7hNCGDXM2v89xviV4vnUah9G3ZnU+5rH23366CD1HYqbgE/svlD88uZDwLf2qvEPhlCrJKmKGcAlSeXsfODHMcY1r7m+Bzj8UDdaDE/HAN3DqG0wea29pHWn9FwPWXE0/DshhP8XQniiePmuEMLPQwgfDiHUhxC+HkJ4MITwUAihrfij3wbOCiHsfg4+DNwfY9yexf9DkpRPBnBJUjk7GfjJPq5/O/A0FKYoD3FbZxWnJ/+Mwoj4XTHG/z6EmuIQ7zeU2m8Z4rZKUXsp6751kG2kWe/ej7f79KeD3H9MjPEDwN8DnwI+AnwSmA9cCrwcYzyDQsj+F4AY407gP4DfLG5jPoUZGpIkDZkBXJJUzrYADXtfEUKYA4wCHiiORp4QQvhCCOGuQbb1oxhjG9AK7AJWDXL/HcBhr7ludPH6UtW+OYRwZgjh70MII0pUexp1Dzbqm2a9ex5vr9N1g9z/ieK/m4AVMcZIYYR+BPBW4AMhhHbgO0BdCGF88f43AR8PIUwExsUYn0CSpINgAJcklbN7gHNDCG8ACCG8BbgZmB9jHADeAXwnxng1g4dCAGKMG4H/BdwcQjjmAHddAZyy+z7FgHwGhRHdUtV+CnBijPHPiyOspag9jbqHtK2U6j0UBxpdf5rCft1twPuBO4FfAcQYnwTGAH8IDHX2giRJe9RlXYAkSfsTY+wMIfwd8F8hhMOAWuATMcZHi3c5FfhB8Xz/QWz3ZyGEfwL+Cdi9YNfZIYQle93tY8AVwD0hhFcojAr/c4zxub23FUI4GvjHGOP5h1D7Ywzxi4P91P4vh1o3cGQI4fZDrHvI08mTrrforOKI9d7eH2M8mFH03W4EbgohPACMBf61+MXDbrcA1wHHHcK2JUlVzgAuSSprMcYFwIIQQhPwI14dWE8Cri9OEX7pANtop7CK997XfeE1tx+1jx99FvjuICW+DKw9xNovB/42hNAaY1x8KLUPo+5th1j3dAphep/Srrf4eBMG+dm973/rXufvA+4rnl8GnFO86ROv+8Ff/8zXga8P9fEkSdqbAVySVK4+FkLYsPvQUTHG9RTC3x4xxsuKZ1+mcNzqQbdTCsWF32bsvkhhRHS/Bqn96v38WMnrhlfVfqh1X7qfu2de7yASqQ9eVeP/5de/F5IkvU4orDsiSZIkSZKS5CJskiRJkiSlwAAuSZIkSVIKDOCSJEmSJKXAAC5JkiRJUgoM4JIkSZIkpcAALkmSJElSCgzgkiRJkiSlwAAuSZIkSVIK/n9xYT4iUSHnoAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"ax1DE,data1DEsub,binsE = ds.plotCut1DE(q = centerHKL, width = width, minPixel = 0.05,\n",
" E1 = EMin, E2 = EMax, backgroundSubtraction = True,\n",
" label = 'Subtracted', plotForeground=True, plotBackground=True)\n",
"ax1DE.grid(True)\n",
"ax1DE.legend()\n",
"ax1DE.get_figure().set_size_inches(14,9)"
]
},
{
"cell_type": "markdown",
"id": "cd57d4a0",
"metadata": {},
"source": [
"Not Too much of a difference can be seen but the subtracted data follows the zero line neatly. Far more important is the background subtraction when performing energy cuts due to the presence of the constant energy stripes (To experimentally remove these see FAQ). A cut through the dispersion at HKL = (1.0,0.0,0.0) is performed and plotted similarly to the constant energy 1D cut.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}